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https://github.com/TMElyralab/MuseTalk.git
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czk32611
125
musetalk/whisper/audio2feature.py
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125
musetalk/whisper/audio2feature.py
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import os
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#import whisper
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from whisper import load_model
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#import whisper.whispher as whiisper
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import soundfile as sf
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import numpy as np
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import time
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import sys
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sys.path.append("..")
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class Audio2Feature():
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def __init__(self, whisper_model_type="tiny",model_path="./checkpoints/wisper_tiny.pt"):
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self.whisper_model_type = whisper_model_type
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self.model = load_model(model_path) #
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def get_sliced_feature(self,feature_array, vid_idx, audio_feat_length= [2,2],fps = 25):
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"""
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Get sliced features based on a given index
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:param feature_array:
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:param start_idx: the start index of the feature
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:param audio_feat_length:
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:return:
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"""
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length = len(feature_array)
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selected_feature = []
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selected_idx = []
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center_idx = int(vid_idx*50/fps)
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left_idx = center_idx-audio_feat_length[0]*2
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right_idx = center_idx + (audio_feat_length[1]+1)*2
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for idx in range(left_idx,right_idx):
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idx = max(0, idx)
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idx = min(length-1, idx)
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x = feature_array[idx]
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selected_feature.append(x)
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selected_idx.append(idx)
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selected_feature = np.concatenate(selected_feature, axis=0)
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selected_feature = selected_feature.reshape(-1, 384)# 50*384
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return selected_feature,selected_idx
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def get_sliced_feature_sparse(self,feature_array, vid_idx, audio_feat_length= [2,2],fps = 25):
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"""
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Get sliced features based on a given index
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:param feature_array:
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:param start_idx: the start index of the feature
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:param audio_feat_length:
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:return:
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"""
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length = len(feature_array)
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selected_feature = []
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selected_idx = []
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for dt in range(-audio_feat_length[0],audio_feat_length[1]+1):
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left_idx = int((vid_idx+dt)*50/fps)
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if left_idx<1 or left_idx>length-1:
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left_idx = max(0, left_idx)
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left_idx = min(length-1, left_idx)
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x = feature_array[left_idx]
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x = x[np.newaxis,:,:]
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x = np.repeat(x, 2, axis=0)
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selected_feature.append(x)
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selected_idx.append(left_idx)
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selected_idx.append(left_idx)
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else:
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x = feature_array[left_idx-1:left_idx+1]
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selected_feature.append(x)
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selected_idx.append(left_idx-1)
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selected_idx.append(left_idx)
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selected_feature = np.concatenate(selected_feature, axis=0)
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selected_feature = selected_feature.reshape(-1, 384)# 50*384
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return selected_feature,selected_idx
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def feature2chunks(self,feature_array,fps,audio_feat_length = [2,2]):
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whisper_chunks = []
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whisper_idx_multiplier = 50./fps
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i = 0
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print(f"video in {fps} FPS, audio idx in 50FPS")
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while 1:
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start_idx = int(i * whisper_idx_multiplier)
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selected_feature,selected_idx = self.get_sliced_feature(feature_array= feature_array,vid_idx = i,audio_feat_length=audio_feat_length,fps=fps)
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#print(f"i:{i},selected_idx {selected_idx}")
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whisper_chunks.append(selected_feature)
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i += 1
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if start_idx>len(feature_array):
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break
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return whisper_chunks
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def audio2feat(self,audio_path):
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# get the sample rate of the audio
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result = self.model.transcribe(audio_path)
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embed_list = []
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for emb in result['segments']:
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encoder_embeddings = emb['encoder_embeddings']
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encoder_embeddings = encoder_embeddings.transpose(0,2,1,3)
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encoder_embeddings = encoder_embeddings.squeeze(0)
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start_idx = int(emb['start'])
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end_idx = int(emb['end'])
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emb_end_idx = int((end_idx - start_idx)/2)
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embed_list.append(encoder_embeddings[:emb_end_idx])
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concatenated_array = np.concatenate(embed_list, axis=0)
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return concatenated_array
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if __name__ == "__main__":
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audio_processor = Audio2Feature(model_path="../../models/whisper/whisper_tiny.pt")
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audio_path = "./test.mp3"
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array = audio_processor.audio2feat(audio_path)
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print(array.shape)
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fps = 25
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whisper_idx_multiplier = 50./fps
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i = 0
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print(f"video in {fps} FPS, audio idx in 50FPS")
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while 1:
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start_idx = int(i * whisper_idx_multiplier)
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selected_feature,selected_idx = audio_processor.get_sliced_feature(feature_array= array,vid_idx = i,audio_feat_length=[2,2],fps=fps)
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print(f"video idx {i},\t audio idx {selected_idx},\t shape {selected_feature.shape}")
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i += 1
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if start_idx>len(array):
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break
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6
musetalk/whisper/requirements.txt
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6
musetalk/whisper/requirements.txt
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numpy
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torch
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tqdm
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more-itertools
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transformers>=4.19.0
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ffmpeg-python==0.2.0
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24
musetalk/whisper/setup.py
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24
musetalk/whisper/setup.py
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import os
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import pkg_resources
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from setuptools import setup, find_packages
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setup(
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name="whisper",
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py_modules=["whisper"],
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version="1.0",
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description="",
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author="OpenAI",
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packages=find_packages(exclude=["tests*"]),
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install_requires=[
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str(r)
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for r in pkg_resources.parse_requirements(
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open(os.path.join(os.path.dirname(__file__), "requirements.txt"))
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)
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],
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entry_points = {
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'console_scripts': ['whisper=whisper.transcribe:cli'],
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},
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include_package_data=True,
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extras_require={'dev': ['pytest']},
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)
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5
musetalk/whisper/whisper.egg-info/PKG-INFO
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5
musetalk/whisper/whisper.egg-info/PKG-INFO
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Metadata-Version: 2.1
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Name: whisper
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Version: 1.0
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Author: OpenAI
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Provides-Extra: dev
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18
musetalk/whisper/whisper.egg-info/SOURCES.txt
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18
musetalk/whisper/whisper.egg-info/SOURCES.txt
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setup.py
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whisper/__init__.py
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whisper/__main__.py
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whisper/audio.py
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whisper/decoding.py
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whisper/model.py
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whisper/tokenizer.py
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whisper/transcribe.py
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whisper/utils.py
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whisper.egg-info/PKG-INFO
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whisper.egg-info/SOURCES.txt
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whisper.egg-info/dependency_links.txt
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whisper.egg-info/entry_points.txt
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whisper.egg-info/requires.txt
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whisper.egg-info/top_level.txt
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whisper/normalizers/__init__.py
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whisper/normalizers/basic.py
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whisper/normalizers/english.py
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1
musetalk/whisper/whisper.egg-info/dependency_links.txt
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1
musetalk/whisper/whisper.egg-info/dependency_links.txt
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2
musetalk/whisper/whisper.egg-info/entry_points.txt
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2
musetalk/whisper/whisper.egg-info/entry_points.txt
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[console_scripts]
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whisper = whisper.transcribe:cli
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9
musetalk/whisper/whisper.egg-info/requires.txt
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9
musetalk/whisper/whisper.egg-info/requires.txt
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numpy
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torch
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tqdm
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more-itertools
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transformers>=4.19.0
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ffmpeg-python==0.2.0
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[dev]
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pytest
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1
musetalk/whisper/whisper.egg-info/top_level.txt
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1
musetalk/whisper/whisper.egg-info/top_level.txt
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whisper
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116
musetalk/whisper/whisper/__init__.py
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116
musetalk/whisper/whisper/__init__.py
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import hashlib
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import io
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import os
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import urllib
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import warnings
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from typing import List, Optional, Union
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import torch
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from tqdm import tqdm
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from .audio import load_audio, log_mel_spectrogram, pad_or_trim
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from .decoding import DecodingOptions, DecodingResult, decode, detect_language
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from .model import Whisper, ModelDimensions
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from .transcribe import transcribe
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_MODELS = {
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"tiny.en": "https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32accea0b295c96e26691aa14d8822fac7d9d27d5dc00b4ca2826dd03/tiny.en.pt",
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"tiny": "https://openaipublic.azureedge.net/main/whisper/models/65147644a518d12f04e32d6f3b26facc3f8dd46e5390956a9424a650c0ce22b9/tiny.pt",
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"base.en": "https://openaipublic.azureedge.net/main/whisper/models/25a8566e1d0c1e2231d1c762132cd20e0f96a85d16145c3a00adf5d1ac670ead/base.en.pt",
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"base": "https://openaipublic.azureedge.net/main/whisper/models/ed3a0b6b1c0edf879ad9b11b1af5a0e6ab5db9205f891f668f8b0e6c6326e34e/base.pt",
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"small.en": "https://openaipublic.azureedge.net/main/whisper/models/f953ad0fd29cacd07d5a9eda5624af0f6bcf2258be67c92b79389873d91e0872/small.en.pt",
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"small": "https://openaipublic.azureedge.net/main/whisper/models/9ecf779972d90ba49c06d968637d720dd632c55bbf19d441fb42bf17a411e794/small.pt",
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"medium.en": "https://openaipublic.azureedge.net/main/whisper/models/d7440d1dc186f76616474e0ff0b3b6b879abc9d1a4926b7adfa41db2d497ab4f/medium.en.pt",
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"medium": "https://openaipublic.azureedge.net/main/whisper/models/345ae4da62f9b3d59415adc60127b97c714f32e89e936602e85993674d08dcb1/medium.pt",
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"large": "https://openaipublic.azureedge.net/main/whisper/models/e4b87e7e0bf463eb8e6956e646f1e277e901512310def2c24bf0e11bd3c28e9a/large.pt",
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"large-v1": "https://openaipublic.azureedge.net/main/whisper/models/e4b87e7e0bf463eb8e6956e646f1e277e901512310def2c24bf0e11bd3c28e9a/large-v1.pt",
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"large-v2": "https://openaipublic.azureedge.net/main/whisper/models/81f7c96c852ee8fc832187b0132e569d6c3065a3252ed18e56effd0b6a73e524/large-v2.pt",
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"large-v3": "https://openaipublic.azureedge.net/main/whisper/models/e5b1a55b89c1367dacf97e3e19bfd829a01529dbfdeefa8caeb59b3f1b81dadb/large-v3.pt",
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}
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def _download(url: str, root: str, in_memory: bool) -> Union[bytes, str]:
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os.makedirs(root, exist_ok=True)
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expected_sha256 = url.split("/")[-2]
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download_target = os.path.join(root, os.path.basename(url))
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if os.path.exists(download_target) and not os.path.isfile(download_target):
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raise RuntimeError(f"{download_target} exists and is not a regular file")
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if os.path.isfile(download_target):
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model_bytes = open(download_target, "rb").read()
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if hashlib.sha256(model_bytes).hexdigest() == expected_sha256:
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return model_bytes if in_memory else download_target
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else:
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warnings.warn(f"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file")
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with urllib.request.urlopen(url) as source, open(download_target, "wb") as output:
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with tqdm(total=int(source.info().get("Content-Length")), ncols=80, unit='iB', unit_scale=True, unit_divisor=1024) as loop:
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while True:
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buffer = source.read(8192)
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if not buffer:
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break
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output.write(buffer)
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loop.update(len(buffer))
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model_bytes = open(download_target, "rb").read()
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if hashlib.sha256(model_bytes).hexdigest() != expected_sha256:
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raise RuntimeError("Model has been downloaded but the SHA256 checksum does not not match. Please retry loading the model.")
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return model_bytes if in_memory else download_target
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def available_models() -> List[str]:
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"""Returns the names of available models"""
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return list(_MODELS.keys())
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def load_model(name: str, device: Optional[Union[str, torch.device]] = None, download_root: str = None, in_memory: bool = False) -> Whisper:
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"""
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Load a Whisper ASR model
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Parameters
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----------
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name : str
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one of the official model names listed by `whisper.available_models()`, or
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path to a model checkpoint containing the model dimensions and the model state_dict.
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device : Union[str, torch.device]
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the PyTorch device to put the model into
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download_root: str
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path to download the model files; by default, it uses "~/.cache/whisper"
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in_memory: bool
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whether to preload the model weights into host memory
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Returns
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-------
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model : Whisper
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The Whisper ASR model instance
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"""
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if device is None:
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device = "cuda" if torch.cuda.is_available() else "cpu"
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if download_root is None:
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download_root = os.getenv(
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"XDG_CACHE_HOME",
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os.path.join(os.path.expanduser("~"), ".cache", "whisper")
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)
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if name in _MODELS:
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checkpoint_file = _download(_MODELS[name], download_root, in_memory)
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elif os.path.isfile(name):
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checkpoint_file = open(name, "rb").read() if in_memory else name
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else:
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raise RuntimeError(f"Model {name} not found; available models = {available_models()}")
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with (io.BytesIO(checkpoint_file) if in_memory else open(checkpoint_file, "rb")) as fp:
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checkpoint = torch.load(fp, map_location=device)
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del checkpoint_file
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dims = ModelDimensions(**checkpoint["dims"])
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model = Whisper(dims)
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model.load_state_dict(checkpoint["model_state_dict"])
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return model.to(device)
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4
musetalk/whisper/whisper/__main__.py
Normal file
4
musetalk/whisper/whisper/__main__.py
Normal file
@@ -0,0 +1,4 @@
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from .transcribe import cli
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cli()
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50001
musetalk/whisper/whisper/assets/gpt2/merges.txt
Normal file
50001
musetalk/whisper/whisper/assets/gpt2/merges.txt
Normal file
File diff suppressed because it is too large
Load Diff
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{"bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "unk_token": "<|endoftext|>"}
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@@ -0,0 +1 @@
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{"unk_token": "<|endoftext|>", "bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "add_prefix_space": false, "model_max_length": 1024, "special_tokens_map_file": null, "name_or_path": "gpt2", "tokenizer_class": "GPT2Tokenizer"}
|
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1
musetalk/whisper/whisper/assets/gpt2/vocab.json
Normal file
1
musetalk/whisper/whisper/assets/gpt2/vocab.json
Normal file
File diff suppressed because one or more lines are too long
BIN
musetalk/whisper/whisper/assets/mel_filters.npz
Executable file
BIN
musetalk/whisper/whisper/assets/mel_filters.npz
Executable file
Binary file not shown.
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{"<|endoftext|>": 50257}
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50000
musetalk/whisper/whisper/assets/multilingual/merges.txt
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50000
musetalk/whisper/whisper/assets/multilingual/merges.txt
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File diff suppressed because it is too large
Load Diff
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||||
{"bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "unk_token": "<|endoftext|>"}
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||||
@@ -0,0 +1 @@
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||||
{"unk_token": {"content": "<|endoftext|>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "bos_token": {"content": "<|endoftext|>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "eos_token": {"content": "<|endoftext|>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "add_prefix_space": false, "model_max_length": 1024, "special_tokens_map_file": null, "name_or_path": "multilingual", "errors": "replace", "tokenizer_class": "GPT2Tokenizer"}
|
||||
1
musetalk/whisper/whisper/assets/multilingual/vocab.json
Normal file
1
musetalk/whisper/whisper/assets/multilingual/vocab.json
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File diff suppressed because one or more lines are too long
125
musetalk/whisper/whisper/audio.py
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125
musetalk/whisper/whisper/audio.py
Normal file
@@ -0,0 +1,125 @@
|
||||
import os
|
||||
from functools import lru_cache
|
||||
from typing import Union
|
||||
|
||||
import ffmpeg
|
||||
import numpy as np
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
|
||||
from .utils import exact_div
|
||||
|
||||
# hard-coded audio hyperparameters
|
||||
SAMPLE_RATE = 16000
|
||||
N_FFT = 400
|
||||
N_MELS = 80
|
||||
HOP_LENGTH = 160
|
||||
CHUNK_LENGTH = 30
|
||||
N_SAMPLES = CHUNK_LENGTH * SAMPLE_RATE # 480000: number of samples in a chunk
|
||||
N_FRAMES = exact_div(N_SAMPLES, HOP_LENGTH) # 3000: number of frames in a mel spectrogram input
|
||||
|
||||
|
||||
def load_audio(file: str, sr: int = SAMPLE_RATE):
|
||||
"""
|
||||
Open an audio file and read as mono waveform, resampling as necessary
|
||||
|
||||
Parameters
|
||||
----------
|
||||
file: str
|
||||
The audio file to open
|
||||
|
||||
sr: int
|
||||
The sample rate to resample the audio if necessary
|
||||
|
||||
Returns
|
||||
-------
|
||||
A NumPy array containing the audio waveform, in float32 dtype.
|
||||
"""
|
||||
try:
|
||||
# This launches a subprocess to decode audio while down-mixing and resampling as necessary.
|
||||
# Requires the ffmpeg CLI and `ffmpeg-python` package to be installed.
|
||||
out, _ = (
|
||||
ffmpeg.input(file, threads=0)
|
||||
.output("-", format="s16le", acodec="pcm_s16le", ac=1, ar=sr)
|
||||
.run(cmd=["ffmpeg", "-nostdin"], capture_stdout=True, capture_stderr=True)
|
||||
)
|
||||
except ffmpeg.Error as e:
|
||||
raise RuntimeError(f"Failed to load audio: {e.stderr.decode()}") from e
|
||||
|
||||
return np.frombuffer(out, np.int16).flatten().astype(np.float32) / 32768.0
|
||||
|
||||
|
||||
def pad_or_trim(array, length: int = N_SAMPLES, *, axis: int = -1):
|
||||
"""
|
||||
Pad or trim the audio array to N_SAMPLES, as expected by the encoder.
|
||||
"""
|
||||
if torch.is_tensor(array):
|
||||
if array.shape[axis] > length:
|
||||
array = array.index_select(dim=axis, index=torch.arange(length))
|
||||
|
||||
if array.shape[axis] < length:
|
||||
pad_widths = [(0, 0)] * array.ndim
|
||||
pad_widths[axis] = (0, length - array.shape[axis])
|
||||
array = F.pad(array, [pad for sizes in pad_widths[::-1] for pad in sizes])
|
||||
else:
|
||||
if array.shape[axis] > length:
|
||||
array = array.take(indices=range(length), axis=axis)
|
||||
|
||||
if array.shape[axis] < length:
|
||||
pad_widths = [(0, 0)] * array.ndim
|
||||
pad_widths[axis] = (0, length - array.shape[axis])
|
||||
array = np.pad(array, pad_widths)
|
||||
|
||||
return array
|
||||
|
||||
|
||||
@lru_cache(maxsize=None)
|
||||
def mel_filters(device, n_mels: int = N_MELS) -> torch.Tensor:
|
||||
"""
|
||||
load the mel filterbank matrix for projecting STFT into a Mel spectrogram.
|
||||
Allows decoupling librosa dependency; saved using:
|
||||
|
||||
np.savez_compressed(
|
||||
"mel_filters.npz",
|
||||
mel_80=librosa.filters.mel(sr=16000, n_fft=400, n_mels=80),
|
||||
)
|
||||
"""
|
||||
assert n_mels == 80, f"Unsupported n_mels: {n_mels}"
|
||||
with np.load(os.path.join(os.path.dirname(__file__), "assets", "mel_filters.npz")) as f:
|
||||
return torch.from_numpy(f[f"mel_{n_mels}"]).to(device)
|
||||
|
||||
|
||||
def log_mel_spectrogram(audio: Union[str, np.ndarray, torch.Tensor], n_mels: int = N_MELS):
|
||||
"""
|
||||
Compute the log-Mel spectrogram of
|
||||
|
||||
Parameters
|
||||
----------
|
||||
audio: Union[str, np.ndarray, torch.Tensor], shape = (*)
|
||||
The path to audio or either a NumPy array or Tensor containing the audio waveform in 16 kHz
|
||||
|
||||
n_mels: int
|
||||
The number of Mel-frequency filters, only 80 is supported
|
||||
|
||||
Returns
|
||||
-------
|
||||
torch.Tensor, shape = (80, n_frames)
|
||||
A Tensor that contains the Mel spectrogram
|
||||
"""
|
||||
if not torch.is_tensor(audio):
|
||||
if isinstance(audio, str):
|
||||
audio = load_audio(audio)
|
||||
audio = torch.from_numpy(audio)
|
||||
|
||||
window = torch.hann_window(N_FFT).to(audio.device)
|
||||
stft = torch.stft(audio, N_FFT, HOP_LENGTH, window=window, return_complex=True)
|
||||
|
||||
magnitudes = stft[:, :-1].abs() ** 2
|
||||
|
||||
filters = mel_filters(audio.device, n_mels)
|
||||
mel_spec = filters @ magnitudes
|
||||
|
||||
log_spec = torch.clamp(mel_spec, min=1e-10).log10()
|
||||
log_spec = torch.maximum(log_spec, log_spec.max() - 8.0)
|
||||
log_spec = (log_spec + 4.0) / 4.0
|
||||
return log_spec
|
||||
729
musetalk/whisper/whisper/decoding.py
Normal file
729
musetalk/whisper/whisper/decoding.py
Normal file
@@ -0,0 +1,729 @@
|
||||
from dataclasses import dataclass, field
|
||||
from typing import Dict, List, Tuple, Iterable, Optional, Sequence, Union, TYPE_CHECKING
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
from torch import Tensor
|
||||
from torch.distributions import Categorical
|
||||
|
||||
from .audio import CHUNK_LENGTH
|
||||
from .tokenizer import Tokenizer, get_tokenizer
|
||||
from .utils import compression_ratio
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from .model import Whisper
|
||||
|
||||
|
||||
@torch.no_grad()
|
||||
def detect_language(model: "Whisper", mel: Tensor, tokenizer: Tokenizer = None) -> Tuple[Tensor, List[dict]]:
|
||||
"""
|
||||
Detect the spoken language in the audio, and return them as list of strings, along with the ids
|
||||
of the most probable language tokens and the probability distribution over all language tokens.
|
||||
This is performed outside the main decode loop in order to not interfere with kv-caching.
|
||||
|
||||
Returns
|
||||
-------
|
||||
language_tokens : Tensor, shape = (n_audio,)
|
||||
ids of the most probable language tokens, which appears after the startoftranscript token.
|
||||
language_probs : List[Dict[str, float]], length = n_audio
|
||||
list of dictionaries containing the probability distribution over all languages.
|
||||
"""
|
||||
if tokenizer is None:
|
||||
tokenizer = get_tokenizer(model.is_multilingual)
|
||||
if tokenizer.language is None or tokenizer.language_token not in tokenizer.sot_sequence:
|
||||
raise ValueError(f"This model doesn't have language tokens so it can't perform lang id")
|
||||
|
||||
single = mel.ndim == 2
|
||||
if single:
|
||||
mel = mel.unsqueeze(0)
|
||||
|
||||
# skip encoder forward pass if already-encoded audio features were given
|
||||
if mel.shape[-2:] != (model.dims.n_audio_ctx, model.dims.n_audio_state):
|
||||
mel = model.encoder(mel)
|
||||
|
||||
# forward pass using a single token, startoftranscript
|
||||
n_audio = mel.shape[0]
|
||||
x = torch.tensor([[tokenizer.sot]] * n_audio).to(mel.device) # [n_audio, 1]
|
||||
logits = model.logits(x, mel)[:, 0]
|
||||
|
||||
# collect detected languages; suppress all non-language tokens
|
||||
mask = torch.ones(logits.shape[-1], dtype=torch.bool)
|
||||
mask[list(tokenizer.all_language_tokens)] = False
|
||||
logits[:, mask] = -np.inf
|
||||
language_tokens = logits.argmax(dim=-1)
|
||||
language_token_probs = logits.softmax(dim=-1).cpu()
|
||||
language_probs = [
|
||||
{
|
||||
c: language_token_probs[i, j].item()
|
||||
for j, c in zip(tokenizer.all_language_tokens, tokenizer.all_language_codes)
|
||||
}
|
||||
for i in range(n_audio)
|
||||
]
|
||||
|
||||
if single:
|
||||
language_tokens = language_tokens[0]
|
||||
language_probs = language_probs[0]
|
||||
|
||||
return language_tokens, language_probs
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class DecodingOptions:
|
||||
task: str = "transcribe" # whether to perform X->X "transcribe" or X->English "translate"
|
||||
language: Optional[str] = None # language that the audio is in; uses detected language if None
|
||||
|
||||
# sampling-related options
|
||||
temperature: float = 0.0
|
||||
sample_len: Optional[int] = None # maximum number of tokens to sample
|
||||
best_of: Optional[int] = None # number of independent samples to collect, when t > 0
|
||||
beam_size: Optional[int] = None # number of beams in beam search, when t == 0
|
||||
patience: Optional[float] = None # patience in beam search (https://arxiv.org/abs/2204.05424)
|
||||
|
||||
# options for ranking generations (either beams or best-of-N samples)
|
||||
length_penalty: Optional[float] = None # "alpha" in Google NMT, None defaults to length norm
|
||||
|
||||
# prompt, prefix, and token suppression
|
||||
prompt: Optional[Union[str, List[int]]] = None # text or tokens for the previous context
|
||||
prefix: Optional[Union[str, List[int]]] = None # text or tokens to prefix the current context
|
||||
suppress_blank: bool = True # this will suppress blank outputs
|
||||
|
||||
# list of tokens ids (or comma-separated token ids) to suppress
|
||||
# "-1" will suppress a set of symbols as defined in `tokenizer.non_speech_tokens()`
|
||||
suppress_tokens: Optional[Union[str, Iterable[int]]] = "-1"
|
||||
|
||||
# timestamp sampling options
|
||||
without_timestamps: bool = False # use <|notimestamps|> to sample text tokens only
|
||||
max_initial_timestamp: Optional[float] = 1.0 # the initial timestamp cannot be later than this
|
||||
|
||||
# implementation details
|
||||
fp16: bool = True # use fp16 for most of the calculation
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class DecodingResult:
|
||||
audio_features: Tensor
|
||||
language: str
|
||||
encoder_embeddings: np.ndarray
|
||||
decoder_embeddings: np.ndarray
|
||||
language_probs: Optional[Dict[str, float]] = None
|
||||
tokens: List[int] = field(default_factory=list)
|
||||
text: str = ""
|
||||
avg_logprob: float = np.nan
|
||||
no_speech_prob: float = np.nan
|
||||
temperature: float = np.nan
|
||||
compression_ratio: float = np.nan
|
||||
|
||||
|
||||
class Inference:
|
||||
def logits(self, tokens: Tensor, audio_features: Tensor) -> Tensor:
|
||||
"""Perform a forward pass on the decoder and return per-token logits"""
|
||||
raise NotImplementedError
|
||||
|
||||
def rearrange_kv_cache(self, source_indices) -> None:
|
||||
"""Update the key-value cache according to the updated beams"""
|
||||
raise NotImplementedError
|
||||
|
||||
def cleanup_caching(self) -> None:
|
||||
"""Clean up any resources or hooks after decoding is finished"""
|
||||
pass
|
||||
|
||||
|
||||
class PyTorchInference(Inference):
|
||||
def __init__(self, model: "Whisper", initial_token_length: int):
|
||||
self.model: "Whisper" = model
|
||||
self.initial_token_length = initial_token_length
|
||||
self.kv_cache = {}
|
||||
self.hooks = []
|
||||
|
||||
def logits(self, tokens: Tensor, audio_features: Tensor, include_embeddings=False) -> Tensor:
|
||||
if not self.kv_cache:
|
||||
self.kv_cache, self.hooks = self.model.install_kv_cache_hooks()
|
||||
|
||||
if tokens.shape[-1] > self.initial_token_length:
|
||||
# only need to use the last token except in the first forward pass
|
||||
tokens = tokens[:, -1:]
|
||||
|
||||
return_val = self.model.decoder(tokens, audio_features,
|
||||
kv_cache=self.kv_cache, include_embeddings=include_embeddings)
|
||||
return return_val
|
||||
|
||||
def cleanup_caching(self):
|
||||
for hook in self.hooks:
|
||||
hook.remove()
|
||||
|
||||
self.kv_cache = {}
|
||||
self.hooks = []
|
||||
|
||||
def rearrange_kv_cache(self, source_indices):
|
||||
for module, tensor in self.kv_cache.items():
|
||||
# update the key/value cache to contain the selected sequences
|
||||
self.kv_cache[module] = tensor[source_indices].detach()
|
||||
|
||||
|
||||
class SequenceRanker:
|
||||
def rank(self, tokens: List[List[Tensor]], sum_logprobs: List[List[float]]) -> List[int]:
|
||||
"""
|
||||
Given a list of groups of samples and their cumulative log probabilities,
|
||||
return the indices of the samples in each group to select as the final result
|
||||
"""
|
||||
raise NotImplementedError
|
||||
|
||||
|
||||
class MaximumLikelihoodRanker(SequenceRanker):
|
||||
"""
|
||||
Select the sample with the highest log probabilities, penalized using either
|
||||
a simple length normalization or Google NMT paper's length penalty
|
||||
"""
|
||||
|
||||
def __init__(self, length_penalty: Optional[float]):
|
||||
self.length_penalty = length_penalty
|
||||
|
||||
def rank(self, tokens: List[List[Tensor]], sum_logprobs: List[List[float]]):
|
||||
def scores(logprobs, lengths):
|
||||
result = []
|
||||
for logprob, length in zip(logprobs, lengths):
|
||||
if self.length_penalty is None:
|
||||
penalty = length
|
||||
else:
|
||||
# from the Google NMT paper
|
||||
penalty = ((5 + length) / 6) ** self.length_penalty
|
||||
result.append(logprob / penalty)
|
||||
return result
|
||||
|
||||
# get the sequence with the highest score
|
||||
lengths = [[len(t) for t in s] for s in tokens]
|
||||
return [np.argmax(scores(p, l)) for p, l in zip(sum_logprobs, lengths)]
|
||||
|
||||
|
||||
class TokenDecoder:
|
||||
def reset(self):
|
||||
"""Initialize any stateful variables for decoding a new sequence"""
|
||||
|
||||
def update(self, tokens: Tensor, logits: Tensor, sum_logprobs: Tensor) -> Tuple[Tensor, bool]:
|
||||
"""Specify how to select the next token, based on the current trace and logits
|
||||
|
||||
Parameters
|
||||
----------
|
||||
tokens : Tensor, shape = (n_batch, current_sequence_length)
|
||||
all tokens in the context so far, including the prefix and sot_sequence tokens
|
||||
|
||||
logits : Tensor, shape = (n_batch, vocab_size)
|
||||
per-token logits of the probability distribution at the current step
|
||||
|
||||
sum_logprobs : Tensor, shape = (n_batch)
|
||||
cumulative log probabilities for each sequence
|
||||
|
||||
Returns
|
||||
-------
|
||||
tokens : Tensor, shape = (n_batch, current_sequence_length + 1)
|
||||
the tokens, appended with the selected next token
|
||||
|
||||
completed : bool
|
||||
True if all sequences has reached the end of text
|
||||
|
||||
"""
|
||||
raise NotImplementedError
|
||||
|
||||
def finalize(
|
||||
self, tokens: Tensor, sum_logprobs: Tensor
|
||||
) -> Tuple[Sequence[Sequence[Tensor]], List[List[float]]]:
|
||||
"""Finalize search and return the final candidate sequences
|
||||
|
||||
Parameters
|
||||
----------
|
||||
tokens : Tensor, shape = (n_audio, n_group, current_sequence_length)
|
||||
all tokens in the context so far, including the prefix and sot_sequence
|
||||
|
||||
sum_logprobs : Tensor, shape = (n_audio, n_group)
|
||||
cumulative log probabilities for each sequence
|
||||
|
||||
Returns
|
||||
-------
|
||||
tokens : Sequence[Sequence[Tensor]], length = n_audio
|
||||
sequence of Tensors containing candidate token sequences, for each audio input
|
||||
|
||||
sum_logprobs : List[List[float]], length = n_audio
|
||||
sequence of cumulative log probabilities corresponding to the above
|
||||
|
||||
"""
|
||||
raise NotImplementedError
|
||||
|
||||
|
||||
class GreedyDecoder(TokenDecoder):
|
||||
def __init__(self, temperature: float, eot: int):
|
||||
self.temperature = temperature
|
||||
self.eot = eot
|
||||
|
||||
def update(self, tokens: Tensor, logits: Tensor, sum_logprobs: Tensor) -> Tuple[Tensor, bool]:
|
||||
temperature = self.temperature
|
||||
if temperature == 0:
|
||||
next_tokens = logits.argmax(dim=-1)
|
||||
else:
|
||||
next_tokens = Categorical(logits=logits / temperature).sample()
|
||||
|
||||
logprobs = F.log_softmax(logits.float(), dim=-1)
|
||||
current_logprobs = logprobs[torch.arange(logprobs.shape[0]), next_tokens]
|
||||
sum_logprobs += current_logprobs * (tokens[:, -1] != self.eot)
|
||||
|
||||
next_tokens[tokens[:, -1] == self.eot] = self.eot
|
||||
tokens = torch.cat([tokens, next_tokens[:, None]], dim=-1)
|
||||
|
||||
completed = (tokens[:, -1] == self.eot).all()
|
||||
return tokens, completed
|
||||
|
||||
def finalize(self, tokens: Tensor, sum_logprobs: Tensor):
|
||||
# make sure each sequence has at least one EOT token at the end
|
||||
tokens = F.pad(tokens, (0, 1), value=self.eot)
|
||||
return tokens, sum_logprobs.tolist()
|
||||
|
||||
|
||||
class BeamSearchDecoder(TokenDecoder):
|
||||
def __init__(self, beam_size: int, eot: int, inference: Inference, patience: Optional[float] = None):
|
||||
self.beam_size = beam_size
|
||||
self.eot = eot
|
||||
self.inference = inference
|
||||
self.patience = patience or 1.0
|
||||
self.max_candidates: int = round(beam_size * self.patience)
|
||||
self.finished_sequences = None
|
||||
|
||||
assert self.max_candidates > 0, f"Invalid beam size ({beam_size}) or patience ({patience})"
|
||||
|
||||
def reset(self):
|
||||
self.finished_sequences = None
|
||||
|
||||
def update(self, tokens: Tensor, logits: Tensor, sum_logprobs: Tensor) -> Tuple[Tensor, bool]:
|
||||
if tokens.shape[0] % self.beam_size != 0:
|
||||
raise ValueError(f"{tokens.shape}[0] % {self.beam_size} != 0")
|
||||
|
||||
n_audio = tokens.shape[0] // self.beam_size
|
||||
if self.finished_sequences is None: # for the first update
|
||||
self.finished_sequences = [{} for _ in range(n_audio)]
|
||||
|
||||
logprobs = F.log_softmax(logits.float(), dim=-1)
|
||||
next_tokens, source_indices, finished_sequences = [], [], []
|
||||
for i in range(n_audio):
|
||||
scores, sources, finished = {}, {}, {}
|
||||
|
||||
# STEP 1: calculate the cumulative log probabilities for possible candidates
|
||||
for j in range(self.beam_size):
|
||||
idx = i * self.beam_size + j
|
||||
prefix = tokens[idx].tolist()
|
||||
for logprob, token in zip(*logprobs[idx].topk(self.beam_size + 1)):
|
||||
new_logprob = (sum_logprobs[idx] + logprob).item()
|
||||
sequence = tuple(prefix + [token.item()])
|
||||
scores[sequence] = new_logprob
|
||||
sources[sequence] = idx
|
||||
|
||||
# STEP 2: rank the candidates and keep the top beam_size sequences for each audio
|
||||
saved = 0
|
||||
for sequence in sorted(scores, key=scores.get, reverse=True):
|
||||
if sequence[-1] == self.eot:
|
||||
finished[sequence] = scores[sequence]
|
||||
else:
|
||||
sum_logprobs[len(next_tokens)] = scores[sequence]
|
||||
next_tokens.append(sequence)
|
||||
source_indices.append(sources[sequence])
|
||||
|
||||
saved += 1
|
||||
if saved == self.beam_size:
|
||||
break
|
||||
|
||||
finished_sequences.append(finished)
|
||||
|
||||
tokens = torch.tensor(next_tokens, device=tokens.device)
|
||||
self.inference.rearrange_kv_cache(source_indices)
|
||||
|
||||
# add newly finished sequences to self.finished_sequences
|
||||
assert len(self.finished_sequences) == len(finished_sequences)
|
||||
for previously_finished, newly_finished in zip(self.finished_sequences, finished_sequences):
|
||||
for seq in sorted(newly_finished, key=newly_finished.get, reverse=True):
|
||||
if len(previously_finished) >= self.max_candidates:
|
||||
break # the candidate list is full
|
||||
previously_finished[seq] = newly_finished[seq]
|
||||
|
||||
# mark as completed if all audio has enough number of samples
|
||||
completed = all(
|
||||
len(sequences) >= self.max_candidates for sequences in self.finished_sequences
|
||||
)
|
||||
return tokens, completed
|
||||
|
||||
def finalize(self, preceding_tokens: Tensor, sum_logprobs: Tensor):
|
||||
# collect all finished sequences, including patience, and add unfinished ones if not enough
|
||||
sum_logprobs = sum_logprobs.cpu()
|
||||
for i, sequences in enumerate(self.finished_sequences):
|
||||
if len(sequences) < self.beam_size: # when not enough sequences are finished
|
||||
for j in list(np.argsort(sum_logprobs[i]))[::-1]:
|
||||
sequence = preceding_tokens[i, j].tolist() + [self.eot]
|
||||
sequences[tuple(sequence)] = sum_logprobs[i][j].item()
|
||||
if len(sequences) >= self.beam_size:
|
||||
break
|
||||
|
||||
tokens: List[List[Tensor]] = [
|
||||
[torch.tensor(seq) for seq in sequences.keys()] for sequences in self.finished_sequences
|
||||
]
|
||||
sum_logprobs: List[List[float]] = [
|
||||
list(sequences.values()) for sequences in self.finished_sequences
|
||||
]
|
||||
return tokens, sum_logprobs
|
||||
|
||||
|
||||
class LogitFilter:
|
||||
def apply(self, logits: Tensor, tokens: Tensor) -> None:
|
||||
"""Apply any filtering or masking to logits in-place
|
||||
|
||||
Parameters
|
||||
----------
|
||||
logits : Tensor, shape = (n_batch, vocab_size)
|
||||
per-token logits of the probability distribution at the current step
|
||||
|
||||
tokens : Tensor, shape = (n_batch, current_sequence_length)
|
||||
all tokens in the context so far, including the prefix and sot_sequence tokens
|
||||
|
||||
"""
|
||||
raise NotImplementedError
|
||||
|
||||
|
||||
class SuppressBlank(LogitFilter):
|
||||
def __init__(self, tokenizer: Tokenizer, sample_begin: int):
|
||||
self.tokenizer = tokenizer
|
||||
self.sample_begin = sample_begin
|
||||
|
||||
def apply(self, logits: Tensor, tokens: Tensor):
|
||||
if tokens.shape[1] == self.sample_begin:
|
||||
logits[:, self.tokenizer.encode(" ") + [self.tokenizer.eot]] = -np.inf
|
||||
|
||||
|
||||
class SuppressTokens(LogitFilter):
|
||||
def __init__(self, suppress_tokens: Sequence[int]):
|
||||
self.suppress_tokens = list(suppress_tokens)
|
||||
|
||||
def apply(self, logits: Tensor, tokens: Tensor):
|
||||
logits[:, self.suppress_tokens] = -np.inf
|
||||
|
||||
|
||||
class ApplyTimestampRules(LogitFilter):
|
||||
def __init__(
|
||||
self, tokenizer: Tokenizer, sample_begin: int, max_initial_timestamp_index: Optional[int]
|
||||
):
|
||||
self.tokenizer = tokenizer
|
||||
self.sample_begin = sample_begin
|
||||
self.max_initial_timestamp_index = max_initial_timestamp_index
|
||||
|
||||
def apply(self, logits: Tensor, tokens: Tensor):
|
||||
# suppress <|notimestamps|> which is handled by without_timestamps
|
||||
if self.tokenizer.no_timestamps is not None:
|
||||
logits[:, self.tokenizer.no_timestamps] = -np.inf
|
||||
|
||||
# timestamps have to appear in pairs, except directly before EOT; mask logits accordingly
|
||||
for k in range(tokens.shape[0]):
|
||||
seq = [t for t in tokens[k, self.sample_begin :].tolist()]
|
||||
last_was_timestamp = len(seq) >= 1 and seq[-1] >= self.tokenizer.timestamp_begin
|
||||
penultimate_was_timestamp = len(seq) < 2 or seq[-2] >= self.tokenizer.timestamp_begin
|
||||
|
||||
if last_was_timestamp:
|
||||
if penultimate_was_timestamp: # has to be non-timestamp
|
||||
logits[k, self.tokenizer.timestamp_begin :] = -np.inf
|
||||
else: # cannot be normal text tokens
|
||||
logits[k, : self.tokenizer.eot] = -np.inf
|
||||
|
||||
# apply the `max_initial_timestamp` option
|
||||
if tokens.shape[1] == self.sample_begin and self.max_initial_timestamp_index is not None:
|
||||
last_allowed = self.tokenizer.timestamp_begin + self.max_initial_timestamp_index
|
||||
logits[:, last_allowed + 1 :] = -np.inf
|
||||
|
||||
# if sum of probability over timestamps is above any other token, sample timestamp
|
||||
logprobs = F.log_softmax(logits.float(), dim=-1)
|
||||
for k in range(tokens.shape[0]):
|
||||
timestamp_logprob = logprobs[k, self.tokenizer.timestamp_begin :].logsumexp(dim=-1)
|
||||
max_text_token_logprob = logprobs[k, : self.tokenizer.timestamp_begin].max()
|
||||
if timestamp_logprob > max_text_token_logprob:
|
||||
logits[k, : self.tokenizer.timestamp_begin] = -np.inf
|
||||
|
||||
|
||||
class DecodingTask:
|
||||
inference: Inference
|
||||
sequence_ranker: SequenceRanker
|
||||
decoder: TokenDecoder
|
||||
logit_filters: List[LogitFilter]
|
||||
|
||||
def __init__(self, model: "Whisper", options: DecodingOptions):
|
||||
self.model = model
|
||||
|
||||
language = options.language or "en"
|
||||
tokenizer = get_tokenizer(model.is_multilingual, language=language, task=options.task)
|
||||
self.tokenizer: Tokenizer = tokenizer
|
||||
self.options: DecodingOptions = self._verify_options(options)
|
||||
|
||||
self.n_group: int = options.beam_size or options.best_of or 1
|
||||
self.n_ctx: int = model.dims.n_text_ctx
|
||||
self.sample_len: int = options.sample_len or model.dims.n_text_ctx // 2
|
||||
|
||||
self.sot_sequence: Tuple[int] = tokenizer.sot_sequence
|
||||
if self.options.without_timestamps:
|
||||
self.sot_sequence = tokenizer.sot_sequence_including_notimestamps
|
||||
|
||||
self.initial_tokens: Tuple[int] = self._get_initial_tokens()
|
||||
self.sample_begin: int = len(self.initial_tokens)
|
||||
self.sot_index: int = self.initial_tokens.index(tokenizer.sot)
|
||||
|
||||
# inference: implements the forward pass through the decoder, including kv caching
|
||||
self.inference = PyTorchInference(model, len(self.initial_tokens))
|
||||
|
||||
# sequence ranker: implements how to rank a group of sampled sequences
|
||||
self.sequence_ranker = MaximumLikelihoodRanker(options.length_penalty)
|
||||
|
||||
# decoder: implements how to select the next tokens, given the autoregressive distribution
|
||||
if options.beam_size is not None:
|
||||
self.decoder = BeamSearchDecoder(
|
||||
options.beam_size, tokenizer.eot, self.inference, options.patience
|
||||
)
|
||||
else:
|
||||
self.decoder = GreedyDecoder(options.temperature, tokenizer.eot)
|
||||
|
||||
# logit filters: applies various rules to suppress or penalize certain tokens
|
||||
self.logit_filters = []
|
||||
if self.options.suppress_blank:
|
||||
self.logit_filters.append(SuppressBlank(self.tokenizer, self.sample_begin))
|
||||
if self.options.suppress_tokens:
|
||||
self.logit_filters.append(SuppressTokens(self._get_suppress_tokens()))
|
||||
if not options.without_timestamps:
|
||||
precision = CHUNK_LENGTH / model.dims.n_audio_ctx # usually 0.02 seconds
|
||||
max_initial_timestamp_index = None
|
||||
if options.max_initial_timestamp:
|
||||
max_initial_timestamp_index = round(self.options.max_initial_timestamp / precision)
|
||||
self.logit_filters.append(
|
||||
ApplyTimestampRules(tokenizer, self.sample_begin, max_initial_timestamp_index)
|
||||
)
|
||||
|
||||
def _verify_options(self, options: DecodingOptions) -> DecodingOptions:
|
||||
if options.beam_size is not None and options.best_of is not None:
|
||||
raise ValueError("beam_size and best_of can't be given together")
|
||||
if options.temperature == 0:
|
||||
if options.best_of is not None:
|
||||
raise ValueError("best_of with greedy sampling (T=0) is not compatible")
|
||||
if options.patience is not None and options.beam_size is None:
|
||||
raise ValueError("patience requires beam_size to be given")
|
||||
if options.length_penalty is not None and not (0 <= options.length_penalty <= 1):
|
||||
raise ValueError("length_penalty (alpha) should be a value between 0 and 1")
|
||||
|
||||
return options
|
||||
|
||||
def _get_initial_tokens(self) -> Tuple[int]:
|
||||
tokens = list(self.sot_sequence)
|
||||
prefix = self.options.prefix
|
||||
prompt = self.options.prompt
|
||||
|
||||
if prefix:
|
||||
prefix_tokens = (
|
||||
self.tokenizer.encode(" " + prefix.strip()) if isinstance(prefix, str) else prefix
|
||||
)
|
||||
if self.sample_len is not None:
|
||||
max_prefix_len = self.n_ctx // 2 - self.sample_len
|
||||
prefix_tokens = prefix_tokens[-max_prefix_len:]
|
||||
tokens = tokens + prefix_tokens
|
||||
|
||||
if prompt:
|
||||
prompt_tokens = (
|
||||
self.tokenizer.encode(" " + prompt.strip()) if isinstance(prompt, str) else prompt
|
||||
)
|
||||
tokens = [self.tokenizer.sot_prev] + prompt_tokens[-(self.n_ctx // 2 - 1) :] + tokens
|
||||
|
||||
return tuple(tokens)
|
||||
|
||||
def _get_suppress_tokens(self) -> Tuple[int]:
|
||||
suppress_tokens = self.options.suppress_tokens
|
||||
|
||||
if isinstance(suppress_tokens, str):
|
||||
suppress_tokens = [int(t) for t in suppress_tokens.split(",")]
|
||||
|
||||
if -1 in suppress_tokens:
|
||||
suppress_tokens = [t for t in suppress_tokens if t >= 0]
|
||||
suppress_tokens.extend(self.tokenizer.non_speech_tokens)
|
||||
elif suppress_tokens is None or len(suppress_tokens) == 0:
|
||||
suppress_tokens = [] # interpret empty string as an empty list
|
||||
else:
|
||||
assert isinstance(suppress_tokens, list), "suppress_tokens must be a list"
|
||||
|
||||
suppress_tokens.extend(
|
||||
[self.tokenizer.sot, self.tokenizer.sot_prev, self.tokenizer.sot_lm]
|
||||
)
|
||||
if self.tokenizer.no_speech is not None:
|
||||
# no-speech probability is collected separately
|
||||
suppress_tokens.append(self.tokenizer.no_speech)
|
||||
|
||||
return tuple(sorted(set(suppress_tokens)))
|
||||
|
||||
def _get_audio_features(self, mel: Tensor, include_embeddings: bool = False):
|
||||
if self.options.fp16:
|
||||
mel = mel.half()
|
||||
|
||||
if mel.shape[-2:] == (self.model.dims.n_audio_ctx, self.model.dims.n_audio_state):
|
||||
# encoded audio features are given; skip audio encoding
|
||||
audio_features = mel
|
||||
else:
|
||||
result = self.model.encoder(mel, include_embeddings)
|
||||
if include_embeddings:
|
||||
audio_features, embeddings = result
|
||||
else:
|
||||
audio_features = result
|
||||
|
||||
if audio_features.dtype != (torch.float16 if self.options.fp16 else torch.float32):
|
||||
return TypeError(f"audio_features has an incorrect dtype: {audio_features.dtype}")
|
||||
|
||||
if include_embeddings:
|
||||
return audio_features, embeddings
|
||||
else:
|
||||
return audio_features
|
||||
|
||||
def _detect_language(self, audio_features: Tensor, tokens: Tensor):
|
||||
languages = [self.options.language] * audio_features.shape[0]
|
||||
lang_probs = None
|
||||
|
||||
if self.options.language is None or self.options.task == "lang_id":
|
||||
lang_tokens, lang_probs = self.model.detect_language(audio_features, self.tokenizer)
|
||||
languages = [max(probs, key=probs.get) for probs in lang_probs]
|
||||
if self.options.language is None:
|
||||
tokens[:, self.sot_index + 1] = lang_tokens # write language tokens
|
||||
|
||||
return languages, lang_probs
|
||||
|
||||
def _main_loop(self, audio_features: Tensor, tokens: Tensor):
|
||||
assert audio_features.shape[0] == tokens.shape[0]
|
||||
n_batch = tokens.shape[0]
|
||||
sum_logprobs: Tensor = torch.zeros(n_batch, device=audio_features.device)
|
||||
no_speech_probs = [np.nan] * n_batch
|
||||
|
||||
try:
|
||||
embeddings = []
|
||||
for i in range(self.sample_len):
|
||||
logits, token_embeddings = self.inference.logits(tokens, audio_features, include_embeddings=True)
|
||||
|
||||
if i == 0 and self.tokenizer.no_speech is not None: # save no_speech_probs
|
||||
probs_at_sot = logits[:, self.sot_index].float().softmax(dim=-1)
|
||||
no_speech_probs = probs_at_sot[:, self.tokenizer.no_speech].tolist()
|
||||
|
||||
# now we need to consider the logits at the last token only
|
||||
logits = logits[:, -1]
|
||||
token_embeddings = token_embeddings[:, :, -1]
|
||||
|
||||
# Append embeddings together
|
||||
embeddings.append(token_embeddings)
|
||||
|
||||
# apply the logit filters, e.g. for suppressing or applying penalty to
|
||||
for logit_filter in self.logit_filters:
|
||||
logit_filter.apply(logits, tokens)
|
||||
|
||||
# expand the tokens tensor with the selected next tokens
|
||||
tokens, completed = self.decoder.update(tokens, logits, sum_logprobs)
|
||||
|
||||
if completed or tokens.shape[-1] > self.n_ctx:
|
||||
break
|
||||
finally:
|
||||
if completed:
|
||||
embeddings = embeddings[:-1]
|
||||
embeddings = np.stack(embeddings, 2)
|
||||
self.inference.cleanup_caching()
|
||||
|
||||
return tokens, sum_logprobs, no_speech_probs, embeddings
|
||||
|
||||
@torch.no_grad()
|
||||
def run(self, mel: Tensor) -> List[DecodingResult]:
|
||||
self.decoder.reset()
|
||||
tokenizer: Tokenizer = self.tokenizer
|
||||
n_audio: int = mel.shape[0]
|
||||
|
||||
# encoder forward pass
|
||||
forward_pass: Tuple[Tensor, np.ndarray] = self._get_audio_features(mel, include_embeddings=True)
|
||||
audio_features, encoder_embeddings = forward_pass
|
||||
tokens: Tensor = torch.tensor([self.initial_tokens]).repeat(n_audio, 1)
|
||||
|
||||
# detect language if requested, overwriting the language token
|
||||
languages, language_probs = self._detect_language(audio_features, tokens)
|
||||
if self.options.task == "lang_id":
|
||||
return [
|
||||
DecodingResult(audio_features=features, language=language, language_probs=probs)
|
||||
for features, language, probs in zip(audio_features, languages, language_probs)
|
||||
]
|
||||
|
||||
# repeat the audio & text tensors by the group size, for beam search or best-of-n sampling
|
||||
audio_features = audio_features.repeat_interleave(self.n_group, dim=0)
|
||||
tokens = tokens.repeat_interleave(self.n_group, dim=0).to(audio_features.device)
|
||||
|
||||
# call the main sampling loop
|
||||
tokens, sum_logprobs, no_speech_probs, decoder_embeddings = self._main_loop(audio_features, tokens)
|
||||
|
||||
# reshape the tensors to have (n_audio, n_group) as the first two dimensions
|
||||
audio_features = audio_features[:: self.n_group]
|
||||
no_speech_probs = no_speech_probs[:: self.n_group]
|
||||
assert audio_features.shape[0] == len(no_speech_probs) == n_audio
|
||||
|
||||
tokens = tokens.reshape(n_audio, self.n_group, -1)
|
||||
sum_logprobs = sum_logprobs.reshape(n_audio, self.n_group)
|
||||
|
||||
# get the final candidates for each group, and slice between the first sampled token and EOT
|
||||
tokens, sum_logprobs = self.decoder.finalize(tokens, sum_logprobs)
|
||||
tokens: List[List[Tensor]] = [
|
||||
[t[self.sample_begin : (t == tokenizer.eot).nonzero()[0, 0]] for t in s] for s in tokens
|
||||
]
|
||||
|
||||
# select the top-ranked sample in each group
|
||||
selected = self.sequence_ranker.rank(tokens, sum_logprobs)
|
||||
tokens: List[List[int]] = [t[i].tolist() for i, t in zip(selected, tokens)]
|
||||
texts: List[str] = [tokenizer.decode(t).strip() for t in tokens]
|
||||
|
||||
sum_logprobs: List[float] = [lp[i] for i, lp in zip(selected, sum_logprobs)]
|
||||
avg_logprobs: List[float] = [lp / (len(t) + 1) for t, lp in zip(tokens, sum_logprobs)]
|
||||
|
||||
fields = (texts, languages, tokens, audio_features, avg_logprobs, no_speech_probs)
|
||||
if len(set(map(len, fields))) != 1:
|
||||
raise RuntimeError(f"inconsistent result lengths: {list(map(len, fields))}")
|
||||
|
||||
return [
|
||||
DecodingResult(
|
||||
audio_features=features,
|
||||
language=language,
|
||||
tokens=tokens,
|
||||
text=text,
|
||||
avg_logprob=avg_logprob,
|
||||
no_speech_prob=no_speech_prob,
|
||||
temperature=self.options.temperature,
|
||||
compression_ratio=compression_ratio(text),
|
||||
encoder_embeddings=encoder_embeddings,
|
||||
decoder_embeddings=decoder_embeddings
|
||||
)
|
||||
for text, language, tokens, features, avg_logprob, no_speech_prob in zip(*fields)
|
||||
]
|
||||
|
||||
|
||||
@torch.no_grad()
|
||||
def decode(model: "Whisper", mel: Tensor, options: DecodingOptions = DecodingOptions()) -> Union[DecodingResult, List[DecodingResult]]:
|
||||
"""
|
||||
Performs decoding of 30-second audio segment(s), provided as Mel spectrogram(s).
|
||||
|
||||
Parameters
|
||||
----------
|
||||
model: Whisper
|
||||
the Whisper model instance
|
||||
|
||||
mel: torch.Tensor, shape = (80, 3000) or (*, 80, 3000)
|
||||
A tensor containing the Mel spectrogram(s)
|
||||
|
||||
options: DecodingOptions
|
||||
A dataclass that contains all necessary options for decoding 30-second segments
|
||||
|
||||
Returns
|
||||
-------
|
||||
result: Union[DecodingResult, List[DecodingResult]]
|
||||
The result(s) of decoding contained in `DecodingResult` dataclass instance(s)
|
||||
"""
|
||||
single = mel.ndim == 2
|
||||
if single:
|
||||
mel = mel.unsqueeze(0)
|
||||
|
||||
result = DecodingTask(model, options).run(mel)
|
||||
|
||||
if single:
|
||||
result = result[0]
|
||||
|
||||
return result
|
||||
290
musetalk/whisper/whisper/model.py
Normal file
290
musetalk/whisper/whisper/model.py
Normal file
@@ -0,0 +1,290 @@
|
||||
from dataclasses import dataclass
|
||||
from typing import Dict
|
||||
from typing import Iterable, Optional
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
from torch import Tensor
|
||||
from torch import nn
|
||||
|
||||
from .transcribe import transcribe as transcribe_function
|
||||
from .decoding import detect_language as detect_language_function, decode as decode_function
|
||||
|
||||
|
||||
@dataclass
|
||||
class ModelDimensions:
|
||||
n_mels: int
|
||||
n_audio_ctx: int
|
||||
n_audio_state: int
|
||||
n_audio_head: int
|
||||
n_audio_layer: int
|
||||
n_vocab: int
|
||||
n_text_ctx: int
|
||||
n_text_state: int
|
||||
n_text_head: int
|
||||
n_text_layer: int
|
||||
|
||||
|
||||
class LayerNorm(nn.LayerNorm):
|
||||
def forward(self, x: Tensor) -> Tensor:
|
||||
return super().forward(x.float()).type(x.dtype)
|
||||
|
||||
|
||||
class Linear(nn.Linear):
|
||||
def forward(self, x: Tensor) -> Tensor:
|
||||
return F.linear(
|
||||
x, self.weight.to(x.dtype), None if self.bias is None else self.bias.to(x.dtype)
|
||||
)
|
||||
|
||||
|
||||
class Conv1d(nn.Conv1d):
|
||||
def _conv_forward(self, x: Tensor, weight: Tensor, bias: Optional[Tensor]) -> Tensor:
|
||||
return super()._conv_forward(
|
||||
x, weight.to(x.dtype), None if bias is None else bias.to(x.dtype)
|
||||
)
|
||||
|
||||
|
||||
def sinusoids(length, channels, max_timescale=10000):
|
||||
"""Returns sinusoids for positional embedding"""
|
||||
assert channels % 2 == 0
|
||||
log_timescale_increment = np.log(max_timescale) / (channels // 2 - 1)
|
||||
inv_timescales = torch.exp(-log_timescale_increment * torch.arange(channels // 2))
|
||||
scaled_time = torch.arange(length)[:, np.newaxis] * inv_timescales[np.newaxis, :]
|
||||
return torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], dim=1)
|
||||
|
||||
|
||||
class MultiHeadAttention(nn.Module):
|
||||
def __init__(self, n_state: int, n_head: int):
|
||||
super().__init__()
|
||||
self.n_head = n_head
|
||||
self.query = Linear(n_state, n_state)
|
||||
self.key = Linear(n_state, n_state, bias=False)
|
||||
self.value = Linear(n_state, n_state)
|
||||
self.out = Linear(n_state, n_state)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
x: Tensor,
|
||||
xa: Optional[Tensor] = None,
|
||||
mask: Optional[Tensor] = None,
|
||||
kv_cache: Optional[dict] = None,
|
||||
):
|
||||
q = self.query(x)
|
||||
|
||||
if kv_cache is None or xa is None:
|
||||
# hooks, if installed (i.e. kv_cache is not None), will prepend the cached kv tensors;
|
||||
# otherwise, perform key/value projections for self- or cross-attention as usual.
|
||||
k = self.key(x if xa is None else xa)
|
||||
v = self.value(x if xa is None else xa)
|
||||
else:
|
||||
# for cross-attention, calculate keys and values once and reuse in subsequent calls.
|
||||
k = kv_cache.get(self.key, self.key(xa))
|
||||
v = kv_cache.get(self.value, self.value(xa))
|
||||
|
||||
wv = self.qkv_attention(q, k, v, mask)
|
||||
return self.out(wv)
|
||||
|
||||
def qkv_attention(self, q: Tensor, k: Tensor, v: Tensor, mask: Optional[Tensor] = None):
|
||||
n_batch, n_ctx, n_state = q.shape
|
||||
scale = (n_state // self.n_head) ** -0.25
|
||||
q = q.view(*q.shape[:2], self.n_head, -1).permute(0, 2, 1, 3) * scale
|
||||
k = k.view(*k.shape[:2], self.n_head, -1).permute(0, 2, 3, 1) * scale
|
||||
v = v.view(*v.shape[:2], self.n_head, -1).permute(0, 2, 1, 3)
|
||||
|
||||
qk = q @ k
|
||||
if mask is not None:
|
||||
qk = qk + mask[:n_ctx, :n_ctx]
|
||||
|
||||
w = F.softmax(qk.float(), dim=-1).to(q.dtype)
|
||||
return (w @ v).permute(0, 2, 1, 3).flatten(start_dim=2)
|
||||
|
||||
|
||||
class ResidualAttentionBlock(nn.Module):
|
||||
def __init__(self, n_state: int, n_head: int, cross_attention: bool = False):
|
||||
super().__init__()
|
||||
|
||||
self.attn = MultiHeadAttention(n_state, n_head)
|
||||
self.attn_ln = LayerNorm(n_state)
|
||||
|
||||
self.cross_attn = MultiHeadAttention(n_state, n_head) if cross_attention else None
|
||||
self.cross_attn_ln = LayerNorm(n_state) if cross_attention else None
|
||||
|
||||
n_mlp = n_state * 4
|
||||
self.mlp = nn.Sequential(Linear(n_state, n_mlp), nn.GELU(), Linear(n_mlp, n_state))
|
||||
self.mlp_ln = LayerNorm(n_state)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
x: Tensor,
|
||||
xa: Optional[Tensor] = None,
|
||||
mask: Optional[Tensor] = None,
|
||||
kv_cache: Optional[dict] = None,
|
||||
):
|
||||
x = x + self.attn(self.attn_ln(x), mask=mask, kv_cache=kv_cache)
|
||||
if self.cross_attn:
|
||||
x = x + self.cross_attn(self.cross_attn_ln(x), xa, kv_cache=kv_cache)
|
||||
x = x + self.mlp(self.mlp_ln(x))
|
||||
return x
|
||||
|
||||
|
||||
class AudioEncoder(nn.Module):
|
||||
def __init__(self, n_mels: int, n_ctx: int, n_state: int, n_head: int, n_layer: int):
|
||||
super().__init__()
|
||||
self.conv1 = Conv1d(n_mels, n_state, kernel_size=3, padding=1)
|
||||
self.conv2 = Conv1d(n_state, n_state, kernel_size=3, stride=2, padding=1)
|
||||
self.register_buffer("positional_embedding", sinusoids(n_ctx, n_state))
|
||||
|
||||
self.blocks: Iterable[ResidualAttentionBlock] = nn.ModuleList(
|
||||
[ResidualAttentionBlock(n_state, n_head) for _ in range(n_layer)]
|
||||
)
|
||||
self.ln_post = LayerNorm(n_state)
|
||||
|
||||
def forward(self, x: Tensor, include_embeddings: bool = False):
|
||||
"""
|
||||
x : torch.Tensor, shape = (batch_size, n_mels, n_ctx)
|
||||
the mel spectrogram of the audio
|
||||
include_embeddings: bool
|
||||
whether to include intermediate steps in the output
|
||||
"""
|
||||
x = F.gelu(self.conv1(x))
|
||||
x = F.gelu(self.conv2(x))
|
||||
x = x.permute(0, 2, 1)
|
||||
|
||||
assert x.shape[1:] == self.positional_embedding.shape, "incorrect audio shape"
|
||||
x = (x + self.positional_embedding).to(x.dtype)
|
||||
|
||||
if include_embeddings:
|
||||
embeddings = [x.cpu().detach().numpy()]
|
||||
|
||||
for block in self.blocks:
|
||||
x = block(x)
|
||||
if include_embeddings:
|
||||
embeddings.append(x.cpu().detach().numpy())
|
||||
|
||||
x = self.ln_post(x)
|
||||
|
||||
if include_embeddings:
|
||||
embeddings = np.stack(embeddings, axis=1)
|
||||
return x, embeddings
|
||||
else:
|
||||
return x
|
||||
|
||||
|
||||
class TextDecoder(nn.Module):
|
||||
def __init__(self, n_vocab: int, n_ctx: int, n_state: int, n_head: int, n_layer: int):
|
||||
super().__init__()
|
||||
|
||||
self.token_embedding = nn.Embedding(n_vocab, n_state)
|
||||
self.positional_embedding = nn.Parameter(torch.empty(n_ctx, n_state))
|
||||
|
||||
self.blocks: Iterable[ResidualAttentionBlock] = nn.ModuleList(
|
||||
[ResidualAttentionBlock(n_state, n_head, cross_attention=True) for _ in range(n_layer)]
|
||||
)
|
||||
self.ln = LayerNorm(n_state)
|
||||
|
||||
mask = torch.empty(n_ctx, n_ctx).fill_(-np.inf).triu_(1)
|
||||
self.register_buffer("mask", mask, persistent=False)
|
||||
|
||||
def forward(self, x: Tensor, xa: Tensor, kv_cache: Optional[dict] = None, include_embeddings: bool = False):
|
||||
"""
|
||||
x : torch.LongTensor, shape = (batch_size, <= n_ctx)
|
||||
the text tokens
|
||||
xa : torch.Tensor, shape = (batch_size, n_mels, n_audio_ctx)
|
||||
the encoded audio features to be attended on
|
||||
include_embeddings : bool
|
||||
Whether to include intermediate values in the output to this function
|
||||
"""
|
||||
offset = next(iter(kv_cache.values())).shape[1] if kv_cache else 0
|
||||
x = self.token_embedding(x) + self.positional_embedding[offset : offset + x.shape[-1]]
|
||||
x = x.to(xa.dtype)
|
||||
|
||||
if include_embeddings:
|
||||
embeddings = [x.cpu().detach().numpy()]
|
||||
|
||||
for block in self.blocks:
|
||||
x = block(x, xa, mask=self.mask, kv_cache=kv_cache)
|
||||
if include_embeddings:
|
||||
embeddings.append(x.cpu().detach().numpy())
|
||||
|
||||
x = self.ln(x)
|
||||
logits = (x @ torch.transpose(self.token_embedding.weight.to(x.dtype), 0, 1)).float()
|
||||
|
||||
if include_embeddings:
|
||||
embeddings = np.stack(embeddings, axis=1)
|
||||
return logits, embeddings
|
||||
else:
|
||||
return logits
|
||||
|
||||
|
||||
class Whisper(nn.Module):
|
||||
def __init__(self, dims: ModelDimensions):
|
||||
super().__init__()
|
||||
self.dims = dims
|
||||
self.encoder = AudioEncoder(
|
||||
self.dims.n_mels,
|
||||
self.dims.n_audio_ctx,
|
||||
self.dims.n_audio_state,
|
||||
self.dims.n_audio_head,
|
||||
self.dims.n_audio_layer,
|
||||
)
|
||||
self.decoder = TextDecoder(
|
||||
self.dims.n_vocab,
|
||||
self.dims.n_text_ctx,
|
||||
self.dims.n_text_state,
|
||||
self.dims.n_text_head,
|
||||
self.dims.n_text_layer,
|
||||
)
|
||||
|
||||
def embed_audio(self, mel: torch.Tensor):
|
||||
return self.encoder.forward(mel)
|
||||
|
||||
def logits(self, tokens: torch.Tensor, audio_features: torch.Tensor):
|
||||
return self.decoder.forward(tokens, audio_features)
|
||||
|
||||
def forward(self, mel: torch.Tensor, tokens: torch.Tensor) -> Dict[str, torch.Tensor]:
|
||||
return self.decoder(tokens, self.encoder(mel))
|
||||
|
||||
@property
|
||||
def device(self):
|
||||
return next(self.parameters()).device
|
||||
|
||||
@property
|
||||
def is_multilingual(self):
|
||||
return self.dims.n_vocab == 51865
|
||||
|
||||
def install_kv_cache_hooks(self, cache: Optional[dict] = None):
|
||||
"""
|
||||
The `MultiHeadAttention` module optionally accepts `kv_cache` which stores the key and value
|
||||
tensors calculated for the previous positions. This method returns a dictionary that stores
|
||||
all caches, and the necessary hooks for the key and value projection modules that save the
|
||||
intermediate tensors to be reused during later calculations.
|
||||
|
||||
Returns
|
||||
-------
|
||||
cache : Dict[nn.Module, torch.Tensor]
|
||||
A dictionary object mapping the key/value projection modules to its cache
|
||||
hooks : List[RemovableHandle]
|
||||
List of PyTorch RemovableHandle objects to stop the hooks to be called
|
||||
"""
|
||||
cache = {**cache} if cache is not None else {}
|
||||
hooks = []
|
||||
|
||||
def save_to_cache(module, _, output):
|
||||
if module not in cache or output.shape[1] > self.decoder.positional_embedding.shape[0]:
|
||||
cache[module] = output # save as-is, for the first token or cross attention
|
||||
else:
|
||||
cache[module] = torch.cat([cache[module], output], dim=1).detach()
|
||||
return cache[module]
|
||||
|
||||
def install_hooks(layer: nn.Module):
|
||||
if isinstance(layer, MultiHeadAttention):
|
||||
hooks.append(layer.key.register_forward_hook(save_to_cache))
|
||||
hooks.append(layer.value.register_forward_hook(save_to_cache))
|
||||
|
||||
self.decoder.apply(install_hooks)
|
||||
return cache, hooks
|
||||
|
||||
detect_language = detect_language_function
|
||||
transcribe = transcribe_function
|
||||
decode = decode_function
|
||||
2
musetalk/whisper/whisper/normalizers/__init__.py
Normal file
2
musetalk/whisper/whisper/normalizers/__init__.py
Normal file
@@ -0,0 +1,2 @@
|
||||
from .basic import BasicTextNormalizer
|
||||
from .english import EnglishTextNormalizer
|
||||
71
musetalk/whisper/whisper/normalizers/basic.py
Normal file
71
musetalk/whisper/whisper/normalizers/basic.py
Normal file
@@ -0,0 +1,71 @@
|
||||
import re
|
||||
import unicodedata
|
||||
|
||||
import regex
|
||||
|
||||
# non-ASCII letters that are not separated by "NFKD" normalization
|
||||
ADDITIONAL_DIACRITICS = {
|
||||
"œ": "oe",
|
||||
"Œ": "OE",
|
||||
"ø": "o",
|
||||
"Ø": "O",
|
||||
"æ": "ae",
|
||||
"Æ": "AE",
|
||||
"ß": "ss",
|
||||
"ẞ": "SS",
|
||||
"đ": "d",
|
||||
"Đ": "D",
|
||||
"ð": "d",
|
||||
"Ð": "D",
|
||||
"þ": "th",
|
||||
"Þ": "th",
|
||||
"ł": "l",
|
||||
"Ł": "L",
|
||||
}
|
||||
|
||||
|
||||
def remove_symbols_and_diacritics(s: str, keep=""):
|
||||
"""
|
||||
Replace any other markers, symbols, and punctuations with a space,
|
||||
and drop any diacritics (category 'Mn' and some manual mappings)
|
||||
"""
|
||||
return "".join(
|
||||
c
|
||||
if c in keep
|
||||
else ADDITIONAL_DIACRITICS[c]
|
||||
if c in ADDITIONAL_DIACRITICS
|
||||
else ""
|
||||
if unicodedata.category(c) == "Mn"
|
||||
else " "
|
||||
if unicodedata.category(c)[0] in "MSP"
|
||||
else c
|
||||
for c in unicodedata.normalize("NFKD", s)
|
||||
)
|
||||
|
||||
|
||||
def remove_symbols(s: str):
|
||||
"""
|
||||
Replace any other markers, symbols, punctuations with a space, keeping diacritics
|
||||
"""
|
||||
return "".join(
|
||||
" " if unicodedata.category(c)[0] in "MSP" else c for c in unicodedata.normalize("NFKC", s)
|
||||
)
|
||||
|
||||
|
||||
class BasicTextNormalizer:
|
||||
def __init__(self, remove_diacritics: bool = False, split_letters: bool = False):
|
||||
self.clean = remove_symbols_and_diacritics if remove_diacritics else remove_symbols
|
||||
self.split_letters = split_letters
|
||||
|
||||
def __call__(self, s: str):
|
||||
s = s.lower()
|
||||
s = re.sub(r"[<\[][^>\]]*[>\]]", "", s) # remove words between brackets
|
||||
s = re.sub(r"\(([^)]+?)\)", "", s) # remove words between parenthesis
|
||||
s = self.clean(s).lower()
|
||||
|
||||
if self.split_letters:
|
||||
s = " ".join(regex.findall(r"\X", s, regex.U))
|
||||
|
||||
s = re.sub(r"\s+", " ", s) # replace any successive whitespace characters with a space
|
||||
|
||||
return s
|
||||
1742
musetalk/whisper/whisper/normalizers/english.json
Normal file
1742
musetalk/whisper/whisper/normalizers/english.json
Normal file
File diff suppressed because it is too large
Load Diff
543
musetalk/whisper/whisper/normalizers/english.py
Normal file
543
musetalk/whisper/whisper/normalizers/english.py
Normal file
@@ -0,0 +1,543 @@
|
||||
import json
|
||||
import os
|
||||
import re
|
||||
from fractions import Fraction
|
||||
from typing import Iterator, List, Match, Optional, Union
|
||||
|
||||
from more_itertools import windowed
|
||||
|
||||
from .basic import remove_symbols_and_diacritics
|
||||
|
||||
|
||||
class EnglishNumberNormalizer:
|
||||
"""
|
||||
Convert any spelled-out numbers into arabic numbers, while handling:
|
||||
|
||||
- remove any commas
|
||||
- keep the suffixes such as: `1960s`, `274th`, `32nd`, etc.
|
||||
- spell out currency symbols after the number. e.g. `$20 million` -> `20000000 dollars`
|
||||
- spell out `one` and `ones`
|
||||
- interpret successive single-digit numbers as nominal: `one oh one` -> `101`
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
|
||||
self.zeros = {"o", "oh", "zero"}
|
||||
self.ones = {
|
||||
name: i
|
||||
for i, name in enumerate(
|
||||
[
|
||||
"one",
|
||||
"two",
|
||||
"three",
|
||||
"four",
|
||||
"five",
|
||||
"six",
|
||||
"seven",
|
||||
"eight",
|
||||
"nine",
|
||||
"ten",
|
||||
"eleven",
|
||||
"twelve",
|
||||
"thirteen",
|
||||
"fourteen",
|
||||
"fifteen",
|
||||
"sixteen",
|
||||
"seventeen",
|
||||
"eighteen",
|
||||
"nineteen",
|
||||
],
|
||||
start=1,
|
||||
)
|
||||
}
|
||||
self.ones_plural = {
|
||||
"sixes" if name == "six" else name + "s": (value, "s")
|
||||
for name, value in self.ones.items()
|
||||
}
|
||||
self.ones_ordinal = {
|
||||
"zeroth": (0, "th"),
|
||||
"first": (1, "st"),
|
||||
"second": (2, "nd"),
|
||||
"third": (3, "rd"),
|
||||
"fifth": (5, "th"),
|
||||
"twelfth": (12, "th"),
|
||||
**{
|
||||
name + ("h" if name.endswith("t") else "th"): (value, "th")
|
||||
for name, value in self.ones.items()
|
||||
if value > 3 and value != 5 and value != 12
|
||||
},
|
||||
}
|
||||
self.ones_suffixed = {**self.ones_plural, **self.ones_ordinal}
|
||||
|
||||
self.tens = {
|
||||
"twenty": 20,
|
||||
"thirty": 30,
|
||||
"forty": 40,
|
||||
"fifty": 50,
|
||||
"sixty": 60,
|
||||
"seventy": 70,
|
||||
"eighty": 80,
|
||||
"ninety": 90,
|
||||
}
|
||||
self.tens_plural = {
|
||||
name.replace("y", "ies"): (value, "s") for name, value in self.tens.items()
|
||||
}
|
||||
self.tens_ordinal = {
|
||||
name.replace("y", "ieth"): (value, "th") for name, value in self.tens.items()
|
||||
}
|
||||
self.tens_suffixed = {**self.tens_plural, **self.tens_ordinal}
|
||||
|
||||
self.multipliers = {
|
||||
"hundred": 100,
|
||||
"thousand": 1_000,
|
||||
"million": 1_000_000,
|
||||
"billion": 1_000_000_000,
|
||||
"trillion": 1_000_000_000_000,
|
||||
"quadrillion": 1_000_000_000_000_000,
|
||||
"quintillion": 1_000_000_000_000_000_000,
|
||||
"sextillion": 1_000_000_000_000_000_000_000,
|
||||
"septillion": 1_000_000_000_000_000_000_000_000,
|
||||
"octillion": 1_000_000_000_000_000_000_000_000_000,
|
||||
"nonillion": 1_000_000_000_000_000_000_000_000_000_000,
|
||||
"decillion": 1_000_000_000_000_000_000_000_000_000_000_000,
|
||||
}
|
||||
self.multipliers_plural = {
|
||||
name + "s": (value, "s") for name, value in self.multipliers.items()
|
||||
}
|
||||
self.multipliers_ordinal = {
|
||||
name + "th": (value, "th") for name, value in self.multipliers.items()
|
||||
}
|
||||
self.multipliers_suffixed = {**self.multipliers_plural, **self.multipliers_ordinal}
|
||||
self.decimals = {*self.ones, *self.tens, *self.zeros}
|
||||
|
||||
self.preceding_prefixers = {
|
||||
"minus": "-",
|
||||
"negative": "-",
|
||||
"plus": "+",
|
||||
"positive": "+",
|
||||
}
|
||||
self.following_prefixers = {
|
||||
"pound": "£",
|
||||
"pounds": "£",
|
||||
"euro": "€",
|
||||
"euros": "€",
|
||||
"dollar": "$",
|
||||
"dollars": "$",
|
||||
"cent": "¢",
|
||||
"cents": "¢",
|
||||
}
|
||||
self.prefixes = set(
|
||||
list(self.preceding_prefixers.values()) + list(self.following_prefixers.values())
|
||||
)
|
||||
self.suffixers = {
|
||||
"per": {"cent": "%"},
|
||||
"percent": "%",
|
||||
}
|
||||
self.specials = {"and", "double", "triple", "point"}
|
||||
|
||||
self.words = set(
|
||||
[
|
||||
key
|
||||
for mapping in [
|
||||
self.zeros,
|
||||
self.ones,
|
||||
self.ones_suffixed,
|
||||
self.tens,
|
||||
self.tens_suffixed,
|
||||
self.multipliers,
|
||||
self.multipliers_suffixed,
|
||||
self.preceding_prefixers,
|
||||
self.following_prefixers,
|
||||
self.suffixers,
|
||||
self.specials,
|
||||
]
|
||||
for key in mapping
|
||||
]
|
||||
)
|
||||
self.literal_words = {"one", "ones"}
|
||||
|
||||
def process_words(self, words: List[str]) -> Iterator[str]:
|
||||
prefix: Optional[str] = None
|
||||
value: Optional[Union[str, int]] = None
|
||||
skip = False
|
||||
|
||||
def to_fraction(s: str):
|
||||
try:
|
||||
return Fraction(s)
|
||||
except ValueError:
|
||||
return None
|
||||
|
||||
def output(result: Union[str, int]):
|
||||
nonlocal prefix, value
|
||||
result = str(result)
|
||||
if prefix is not None:
|
||||
result = prefix + result
|
||||
value = None
|
||||
prefix = None
|
||||
return result
|
||||
|
||||
if len(words) == 0:
|
||||
return
|
||||
|
||||
for prev, current, next in windowed([None] + words + [None], 3):
|
||||
if skip:
|
||||
skip = False
|
||||
continue
|
||||
|
||||
next_is_numeric = next is not None and re.match(r"^\d+(\.\d+)?$", next)
|
||||
has_prefix = current[0] in self.prefixes
|
||||
current_without_prefix = current[1:] if has_prefix else current
|
||||
if re.match(r"^\d+(\.\d+)?$", current_without_prefix):
|
||||
# arabic numbers (potentially with signs and fractions)
|
||||
f = to_fraction(current_without_prefix)
|
||||
assert f is not None
|
||||
if value is not None:
|
||||
if isinstance(value, str) and value.endswith("."):
|
||||
# concatenate decimals / ip address components
|
||||
value = str(value) + str(current)
|
||||
continue
|
||||
else:
|
||||
yield output(value)
|
||||
|
||||
prefix = current[0] if has_prefix else prefix
|
||||
if f.denominator == 1:
|
||||
value = f.numerator # store integers as int
|
||||
else:
|
||||
value = current_without_prefix
|
||||
elif current not in self.words:
|
||||
# non-numeric words
|
||||
if value is not None:
|
||||
yield output(value)
|
||||
yield output(current)
|
||||
elif current in self.zeros:
|
||||
value = str(value or "") + "0"
|
||||
elif current in self.ones:
|
||||
ones = self.ones[current]
|
||||
|
||||
if value is None:
|
||||
value = ones
|
||||
elif isinstance(value, str) or prev in self.ones:
|
||||
if prev in self.tens and ones < 10: # replace the last zero with the digit
|
||||
assert value[-1] == "0"
|
||||
value = value[:-1] + str(ones)
|
||||
else:
|
||||
value = str(value) + str(ones)
|
||||
elif ones < 10:
|
||||
if value % 10 == 0:
|
||||
value += ones
|
||||
else:
|
||||
value = str(value) + str(ones)
|
||||
else: # eleven to nineteen
|
||||
if value % 100 == 0:
|
||||
value += ones
|
||||
else:
|
||||
value = str(value) + str(ones)
|
||||
elif current in self.ones_suffixed:
|
||||
# ordinal or cardinal; yield the number right away
|
||||
ones, suffix = self.ones_suffixed[current]
|
||||
if value is None:
|
||||
yield output(str(ones) + suffix)
|
||||
elif isinstance(value, str) or prev in self.ones:
|
||||
if prev in self.tens and ones < 10:
|
||||
assert value[-1] == "0"
|
||||
yield output(value[:-1] + str(ones) + suffix)
|
||||
else:
|
||||
yield output(str(value) + str(ones) + suffix)
|
||||
elif ones < 10:
|
||||
if value % 10 == 0:
|
||||
yield output(str(value + ones) + suffix)
|
||||
else:
|
||||
yield output(str(value) + str(ones) + suffix)
|
||||
else: # eleven to nineteen
|
||||
if value % 100 == 0:
|
||||
yield output(str(value + ones) + suffix)
|
||||
else:
|
||||
yield output(str(value) + str(ones) + suffix)
|
||||
value = None
|
||||
elif current in self.tens:
|
||||
tens = self.tens[current]
|
||||
if value is None:
|
||||
value = tens
|
||||
elif isinstance(value, str):
|
||||
value = str(value) + str(tens)
|
||||
else:
|
||||
if value % 100 == 0:
|
||||
value += tens
|
||||
else:
|
||||
value = str(value) + str(tens)
|
||||
elif current in self.tens_suffixed:
|
||||
# ordinal or cardinal; yield the number right away
|
||||
tens, suffix = self.tens_suffixed[current]
|
||||
if value is None:
|
||||
yield output(str(tens) + suffix)
|
||||
elif isinstance(value, str):
|
||||
yield output(str(value) + str(tens) + suffix)
|
||||
else:
|
||||
if value % 100 == 0:
|
||||
yield output(str(value + tens) + suffix)
|
||||
else:
|
||||
yield output(str(value) + str(tens) + suffix)
|
||||
elif current in self.multipliers:
|
||||
multiplier = self.multipliers[current]
|
||||
if value is None:
|
||||
value = multiplier
|
||||
elif isinstance(value, str) or value == 0:
|
||||
f = to_fraction(value)
|
||||
p = f * multiplier if f is not None else None
|
||||
if f is not None and p.denominator == 1:
|
||||
value = p.numerator
|
||||
else:
|
||||
yield output(value)
|
||||
value = multiplier
|
||||
else:
|
||||
before = value // 1000 * 1000
|
||||
residual = value % 1000
|
||||
value = before + residual * multiplier
|
||||
elif current in self.multipliers_suffixed:
|
||||
multiplier, suffix = self.multipliers_suffixed[current]
|
||||
if value is None:
|
||||
yield output(str(multiplier) + suffix)
|
||||
elif isinstance(value, str):
|
||||
f = to_fraction(value)
|
||||
p = f * multiplier if f is not None else None
|
||||
if f is not None and p.denominator == 1:
|
||||
yield output(str(p.numerator) + suffix)
|
||||
else:
|
||||
yield output(value)
|
||||
yield output(str(multiplier) + suffix)
|
||||
else: # int
|
||||
before = value // 1000 * 1000
|
||||
residual = value % 1000
|
||||
value = before + residual * multiplier
|
||||
yield output(str(value) + suffix)
|
||||
value = None
|
||||
elif current in self.preceding_prefixers:
|
||||
# apply prefix (positive, minus, etc.) if it precedes a number
|
||||
if value is not None:
|
||||
yield output(value)
|
||||
|
||||
if next in self.words or next_is_numeric:
|
||||
prefix = self.preceding_prefixers[current]
|
||||
else:
|
||||
yield output(current)
|
||||
elif current in self.following_prefixers:
|
||||
# apply prefix (dollars, cents, etc.) only after a number
|
||||
if value is not None:
|
||||
prefix = self.following_prefixers[current]
|
||||
yield output(value)
|
||||
else:
|
||||
yield output(current)
|
||||
elif current in self.suffixers:
|
||||
# apply suffix symbols (percent -> '%')
|
||||
if value is not None:
|
||||
suffix = self.suffixers[current]
|
||||
if isinstance(suffix, dict):
|
||||
if next in suffix:
|
||||
yield output(str(value) + suffix[next])
|
||||
skip = True
|
||||
else:
|
||||
yield output(value)
|
||||
yield output(current)
|
||||
else:
|
||||
yield output(str(value) + suffix)
|
||||
else:
|
||||
yield output(current)
|
||||
elif current in self.specials:
|
||||
if next not in self.words and not next_is_numeric:
|
||||
# apply special handling only if the next word can be numeric
|
||||
if value is not None:
|
||||
yield output(value)
|
||||
yield output(current)
|
||||
elif current == "and":
|
||||
# ignore "and" after hundreds, thousands, etc.
|
||||
if prev not in self.multipliers:
|
||||
if value is not None:
|
||||
yield output(value)
|
||||
yield output(current)
|
||||
elif current == "double" or current == "triple":
|
||||
if next in self.ones or next in self.zeros:
|
||||
repeats = 2 if current == "double" else 3
|
||||
ones = self.ones.get(next, 0)
|
||||
value = str(value or "") + str(ones) * repeats
|
||||
skip = True
|
||||
else:
|
||||
if value is not None:
|
||||
yield output(value)
|
||||
yield output(current)
|
||||
elif current == "point":
|
||||
if next in self.decimals or next_is_numeric:
|
||||
value = str(value or "") + "."
|
||||
else:
|
||||
# should all have been covered at this point
|
||||
raise ValueError(f"Unexpected token: {current}")
|
||||
else:
|
||||
# all should have been covered at this point
|
||||
raise ValueError(f"Unexpected token: {current}")
|
||||
|
||||
if value is not None:
|
||||
yield output(value)
|
||||
|
||||
def preprocess(self, s: str):
|
||||
# replace "<number> and a half" with "<number> point five"
|
||||
results = []
|
||||
|
||||
segments = re.split(r"\band\s+a\s+half\b", s)
|
||||
for i, segment in enumerate(segments):
|
||||
if len(segment.strip()) == 0:
|
||||
continue
|
||||
if i == len(segments) - 1:
|
||||
results.append(segment)
|
||||
else:
|
||||
results.append(segment)
|
||||
last_word = segment.rsplit(maxsplit=2)[-1]
|
||||
if last_word in self.decimals or last_word in self.multipliers:
|
||||
results.append("point five")
|
||||
else:
|
||||
results.append("and a half")
|
||||
|
||||
s = " ".join(results)
|
||||
|
||||
# put a space at number/letter boundary
|
||||
s = re.sub(r"([a-z])([0-9])", r"\1 \2", s)
|
||||
s = re.sub(r"([0-9])([a-z])", r"\1 \2", s)
|
||||
|
||||
# but remove spaces which could be a suffix
|
||||
s = re.sub(r"([0-9])\s+(st|nd|rd|th|s)\b", r"\1\2", s)
|
||||
|
||||
return s
|
||||
|
||||
def postprocess(self, s: str):
|
||||
def combine_cents(m: Match):
|
||||
try:
|
||||
currency = m.group(1)
|
||||
integer = m.group(2)
|
||||
cents = int(m.group(3))
|
||||
return f"{currency}{integer}.{cents:02d}"
|
||||
except ValueError:
|
||||
return m.string
|
||||
|
||||
def extract_cents(m: Match):
|
||||
try:
|
||||
return f"¢{int(m.group(1))}"
|
||||
except ValueError:
|
||||
return m.string
|
||||
|
||||
# apply currency postprocessing; "$2 and ¢7" -> "$2.07"
|
||||
s = re.sub(r"([€£$])([0-9]+) (?:and )?¢([0-9]{1,2})\b", combine_cents, s)
|
||||
s = re.sub(r"[€£$]0.([0-9]{1,2})\b", extract_cents, s)
|
||||
|
||||
# write "one(s)" instead of "1(s)", just for the readability
|
||||
s = re.sub(r"\b1(s?)\b", r"one\1", s)
|
||||
|
||||
return s
|
||||
|
||||
def __call__(self, s: str):
|
||||
s = self.preprocess(s)
|
||||
s = " ".join(word for word in self.process_words(s.split()) if word is not None)
|
||||
s = self.postprocess(s)
|
||||
|
||||
return s
|
||||
|
||||
|
||||
class EnglishSpellingNormalizer:
|
||||
"""
|
||||
Applies British-American spelling mappings as listed in [1].
|
||||
|
||||
[1] https://www.tysto.com/uk-us-spelling-list.html
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
mapping_path = os.path.join(os.path.dirname(__file__), "english.json")
|
||||
self.mapping = json.load(open(mapping_path))
|
||||
|
||||
def __call__(self, s: str):
|
||||
return " ".join(self.mapping.get(word, word) for word in s.split())
|
||||
|
||||
|
||||
class EnglishTextNormalizer:
|
||||
def __init__(self):
|
||||
self.ignore_patterns = r"\b(hmm|mm|mhm|mmm|uh|um)\b"
|
||||
self.replacers = {
|
||||
# common contractions
|
||||
r"\bwon't\b": "will not",
|
||||
r"\bcan't\b": "can not",
|
||||
r"\blet's\b": "let us",
|
||||
r"\bain't\b": "aint",
|
||||
r"\by'all\b": "you all",
|
||||
r"\bwanna\b": "want to",
|
||||
r"\bgotta\b": "got to",
|
||||
r"\bgonna\b": "going to",
|
||||
r"\bi'ma\b": "i am going to",
|
||||
r"\bimma\b": "i am going to",
|
||||
r"\bwoulda\b": "would have",
|
||||
r"\bcoulda\b": "could have",
|
||||
r"\bshoulda\b": "should have",
|
||||
r"\bma'am\b": "madam",
|
||||
# contractions in titles/prefixes
|
||||
r"\bmr\b": "mister ",
|
||||
r"\bmrs\b": "missus ",
|
||||
r"\bst\b": "saint ",
|
||||
r"\bdr\b": "doctor ",
|
||||
r"\bprof\b": "professor ",
|
||||
r"\bcapt\b": "captain ",
|
||||
r"\bgov\b": "governor ",
|
||||
r"\bald\b": "alderman ",
|
||||
r"\bgen\b": "general ",
|
||||
r"\bsen\b": "senator ",
|
||||
r"\brep\b": "representative ",
|
||||
r"\bpres\b": "president ",
|
||||
r"\brev\b": "reverend ",
|
||||
r"\bhon\b": "honorable ",
|
||||
r"\basst\b": "assistant ",
|
||||
r"\bassoc\b": "associate ",
|
||||
r"\blt\b": "lieutenant ",
|
||||
r"\bcol\b": "colonel ",
|
||||
r"\bjr\b": "junior ",
|
||||
r"\bsr\b": "senior ",
|
||||
r"\besq\b": "esquire ",
|
||||
# prefect tenses, ideally it should be any past participles, but it's harder..
|
||||
r"'d been\b": " had been",
|
||||
r"'s been\b": " has been",
|
||||
r"'d gone\b": " had gone",
|
||||
r"'s gone\b": " has gone",
|
||||
r"'d done\b": " had done", # "'s done" is ambiguous
|
||||
r"'s got\b": " has got",
|
||||
# general contractions
|
||||
r"n't\b": " not",
|
||||
r"'re\b": " are",
|
||||
r"'s\b": " is",
|
||||
r"'d\b": " would",
|
||||
r"'ll\b": " will",
|
||||
r"'t\b": " not",
|
||||
r"'ve\b": " have",
|
||||
r"'m\b": " am",
|
||||
}
|
||||
self.standardize_numbers = EnglishNumberNormalizer()
|
||||
self.standardize_spellings = EnglishSpellingNormalizer()
|
||||
|
||||
def __call__(self, s: str):
|
||||
s = s.lower()
|
||||
|
||||
s = re.sub(r"[<\[][^>\]]*[>\]]", "", s) # remove words between brackets
|
||||
s = re.sub(r"\(([^)]+?)\)", "", s) # remove words between parenthesis
|
||||
s = re.sub(self.ignore_patterns, "", s)
|
||||
s = re.sub(r"\s+'", "'", s) # standardize when there's a space before an apostrophe
|
||||
|
||||
for pattern, replacement in self.replacers.items():
|
||||
s = re.sub(pattern, replacement, s)
|
||||
|
||||
s = re.sub(r"(\d),(\d)", r"\1\2", s) # remove commas between digits
|
||||
s = re.sub(r"\.([^0-9]|$)", r" \1", s) # remove periods not followed by numbers
|
||||
s = remove_symbols_and_diacritics(s, keep=".%$¢€£") # keep some symbols for numerics
|
||||
|
||||
s = self.standardize_numbers(s)
|
||||
s = self.standardize_spellings(s)
|
||||
|
||||
# now remove prefix/suffix symbols that are not preceded/followed by numbers
|
||||
s = re.sub(r"[.$¢€£]([^0-9])", r" \1", s)
|
||||
s = re.sub(r"([^0-9])%", r"\1 ", s)
|
||||
|
||||
s = re.sub(r"\s+", " ", s) # replace any successive whitespace characters with a space
|
||||
|
||||
return s
|
||||
331
musetalk/whisper/whisper/tokenizer.py
Normal file
331
musetalk/whisper/whisper/tokenizer.py
Normal file
@@ -0,0 +1,331 @@
|
||||
import os
|
||||
from dataclasses import dataclass
|
||||
from functools import lru_cache
|
||||
from typing import List, Optional, Tuple, Union
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
from transformers import GPT2TokenizerFast
|
||||
|
||||
LANGUAGES = {
|
||||
"en": "english",
|
||||
"zh": "chinese",
|
||||
"de": "german",
|
||||
"es": "spanish",
|
||||
"ru": "russian",
|
||||
"ko": "korean",
|
||||
"fr": "french",
|
||||
"ja": "japanese",
|
||||
"pt": "portuguese",
|
||||
"tr": "turkish",
|
||||
"pl": "polish",
|
||||
"ca": "catalan",
|
||||
"nl": "dutch",
|
||||
"ar": "arabic",
|
||||
"sv": "swedish",
|
||||
"it": "italian",
|
||||
"id": "indonesian",
|
||||
"hi": "hindi",
|
||||
"fi": "finnish",
|
||||
"vi": "vietnamese",
|
||||
"iw": "hebrew",
|
||||
"uk": "ukrainian",
|
||||
"el": "greek",
|
||||
"ms": "malay",
|
||||
"cs": "czech",
|
||||
"ro": "romanian",
|
||||
"da": "danish",
|
||||
"hu": "hungarian",
|
||||
"ta": "tamil",
|
||||
"no": "norwegian",
|
||||
"th": "thai",
|
||||
"ur": "urdu",
|
||||
"hr": "croatian",
|
||||
"bg": "bulgarian",
|
||||
"lt": "lithuanian",
|
||||
"la": "latin",
|
||||
"mi": "maori",
|
||||
"ml": "malayalam",
|
||||
"cy": "welsh",
|
||||
"sk": "slovak",
|
||||
"te": "telugu",
|
||||
"fa": "persian",
|
||||
"lv": "latvian",
|
||||
"bn": "bengali",
|
||||
"sr": "serbian",
|
||||
"az": "azerbaijani",
|
||||
"sl": "slovenian",
|
||||
"kn": "kannada",
|
||||
"et": "estonian",
|
||||
"mk": "macedonian",
|
||||
"br": "breton",
|
||||
"eu": "basque",
|
||||
"is": "icelandic",
|
||||
"hy": "armenian",
|
||||
"ne": "nepali",
|
||||
"mn": "mongolian",
|
||||
"bs": "bosnian",
|
||||
"kk": "kazakh",
|
||||
"sq": "albanian",
|
||||
"sw": "swahili",
|
||||
"gl": "galician",
|
||||
"mr": "marathi",
|
||||
"pa": "punjabi",
|
||||
"si": "sinhala",
|
||||
"km": "khmer",
|
||||
"sn": "shona",
|
||||
"yo": "yoruba",
|
||||
"so": "somali",
|
||||
"af": "afrikaans",
|
||||
"oc": "occitan",
|
||||
"ka": "georgian",
|
||||
"be": "belarusian",
|
||||
"tg": "tajik",
|
||||
"sd": "sindhi",
|
||||
"gu": "gujarati",
|
||||
"am": "amharic",
|
||||
"yi": "yiddish",
|
||||
"lo": "lao",
|
||||
"uz": "uzbek",
|
||||
"fo": "faroese",
|
||||
"ht": "haitian creole",
|
||||
"ps": "pashto",
|
||||
"tk": "turkmen",
|
||||
"nn": "nynorsk",
|
||||
"mt": "maltese",
|
||||
"sa": "sanskrit",
|
||||
"lb": "luxembourgish",
|
||||
"my": "myanmar",
|
||||
"bo": "tibetan",
|
||||
"tl": "tagalog",
|
||||
"mg": "malagasy",
|
||||
"as": "assamese",
|
||||
"tt": "tatar",
|
||||
"haw": "hawaiian",
|
||||
"ln": "lingala",
|
||||
"ha": "hausa",
|
||||
"ba": "bashkir",
|
||||
"jw": "javanese",
|
||||
"su": "sundanese",
|
||||
}
|
||||
|
||||
# language code lookup by name, with a few language aliases
|
||||
TO_LANGUAGE_CODE = {
|
||||
**{language: code for code, language in LANGUAGES.items()},
|
||||
"burmese": "my",
|
||||
"valencian": "ca",
|
||||
"flemish": "nl",
|
||||
"haitian": "ht",
|
||||
"letzeburgesch": "lb",
|
||||
"pushto": "ps",
|
||||
"panjabi": "pa",
|
||||
"moldavian": "ro",
|
||||
"moldovan": "ro",
|
||||
"sinhalese": "si",
|
||||
"castilian": "es",
|
||||
}
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class Tokenizer:
|
||||
"""A thin wrapper around `GPT2TokenizerFast` providing quick access to special tokens"""
|
||||
|
||||
tokenizer: "GPT2TokenizerFast"
|
||||
language: Optional[str]
|
||||
sot_sequence: Tuple[int]
|
||||
|
||||
def encode(self, text, **kwargs):
|
||||
return self.tokenizer.encode(text, **kwargs)
|
||||
|
||||
def decode(self, token_ids: Union[int, List[int], np.ndarray, torch.Tensor], **kwargs):
|
||||
return self.tokenizer.decode(token_ids, **kwargs)
|
||||
|
||||
def decode_with_timestamps(self, tokens) -> str:
|
||||
"""
|
||||
Timestamp tokens are above the special tokens' id range and are ignored by `decode()`.
|
||||
This method decodes given tokens with timestamps tokens annotated, e.g. "<|1.08|>".
|
||||
"""
|
||||
outputs = [[]]
|
||||
for token in tokens:
|
||||
if token >= self.timestamp_begin:
|
||||
timestamp = f"<|{(token - self.timestamp_begin) * 0.02:.2f}|>"
|
||||
outputs.append(timestamp)
|
||||
outputs.append([])
|
||||
else:
|
||||
outputs[-1].append(token)
|
||||
outputs = [s if isinstance(s, str) else self.tokenizer.decode(s) for s in outputs]
|
||||
return "".join(outputs)
|
||||
|
||||
@property
|
||||
@lru_cache()
|
||||
def eot(self) -> int:
|
||||
return self.tokenizer.eos_token_id
|
||||
|
||||
@property
|
||||
@lru_cache()
|
||||
def sot(self) -> int:
|
||||
return self._get_single_token_id("<|startoftranscript|>")
|
||||
|
||||
@property
|
||||
@lru_cache()
|
||||
def sot_lm(self) -> int:
|
||||
return self._get_single_token_id("<|startoflm|>")
|
||||
|
||||
@property
|
||||
@lru_cache()
|
||||
def sot_prev(self) -> int:
|
||||
return self._get_single_token_id("<|startofprev|>")
|
||||
|
||||
@property
|
||||
@lru_cache()
|
||||
def no_speech(self) -> int:
|
||||
return self._get_single_token_id("<|nospeech|>")
|
||||
|
||||
@property
|
||||
@lru_cache()
|
||||
def no_timestamps(self) -> int:
|
||||
return self._get_single_token_id("<|notimestamps|>")
|
||||
|
||||
@property
|
||||
@lru_cache()
|
||||
def timestamp_begin(self) -> int:
|
||||
return self.tokenizer.all_special_ids[-1] + 1
|
||||
|
||||
@property
|
||||
@lru_cache()
|
||||
def language_token(self) -> int:
|
||||
"""Returns the token id corresponding to the value of the `language` field"""
|
||||
if self.language is None:
|
||||
raise ValueError(f"This tokenizer does not have language token configured")
|
||||
|
||||
additional_tokens = dict(
|
||||
zip(
|
||||
self.tokenizer.additional_special_tokens,
|
||||
self.tokenizer.additional_special_tokens_ids,
|
||||
)
|
||||
)
|
||||
candidate = f"<|{self.language}|>"
|
||||
if candidate in additional_tokens:
|
||||
return additional_tokens[candidate]
|
||||
|
||||
raise KeyError(f"Language {self.language} not found in tokenizer.")
|
||||
|
||||
@property
|
||||
@lru_cache()
|
||||
def all_language_tokens(self) -> Tuple[int]:
|
||||
result = []
|
||||
for token, token_id in zip(
|
||||
self.tokenizer.additional_special_tokens,
|
||||
self.tokenizer.additional_special_tokens_ids,
|
||||
):
|
||||
if token.strip("<|>") in LANGUAGES:
|
||||
result.append(token_id)
|
||||
return tuple(result)
|
||||
|
||||
@property
|
||||
@lru_cache()
|
||||
def all_language_codes(self) -> Tuple[str]:
|
||||
return tuple(self.decode([l]).strip("<|>") for l in self.all_language_tokens)
|
||||
|
||||
@property
|
||||
@lru_cache()
|
||||
def sot_sequence_including_notimestamps(self) -> Tuple[int]:
|
||||
return tuple(list(self.sot_sequence) + [self.no_timestamps])
|
||||
|
||||
@property
|
||||
@lru_cache()
|
||||
def non_speech_tokens(self) -> Tuple[int]:
|
||||
"""
|
||||
Returns the list of tokens to suppress in order to avoid any speaker tags or non-speech
|
||||
annotations, to prevent sampling texts that are not actually spoken in the audio, e.g.
|
||||
|
||||
- ♪♪♪
|
||||
- ( SPEAKING FOREIGN LANGUAGE )
|
||||
- [DAVID] Hey there,
|
||||
|
||||
keeping basic punctuations like commas, periods, question marks, exclamation points, etc.
|
||||
"""
|
||||
symbols = list("\"#()*+/:;<=>@[\\]^_`{|}~「」『』")
|
||||
symbols += "<< >> <<< >>> -- --- -( -[ (' (\" (( )) ((( ))) [[ ]] {{ }} ♪♪ ♪♪♪".split()
|
||||
|
||||
# symbols that may be a single token or multiple tokens depending on the tokenizer.
|
||||
# In case they're multiple tokens, suppress the first token, which is safe because:
|
||||
# These are between U+2640 and U+267F miscellaneous symbols that are okay to suppress
|
||||
# in generations, and in the 3-byte UTF-8 representation they share the first two bytes.
|
||||
miscellaneous = set("♩♪♫♬♭♮♯")
|
||||
assert all(0x2640 <= ord(c) <= 0x267F for c in miscellaneous)
|
||||
|
||||
# allow hyphens "-" and single quotes "'" between words, but not at the beginning of a word
|
||||
result = {self.tokenizer.encode(" -")[0], self.tokenizer.encode(" '")[0]}
|
||||
for symbol in symbols + list(miscellaneous):
|
||||
for tokens in [self.tokenizer.encode(symbol), self.tokenizer.encode(" " + symbol)]:
|
||||
if len(tokens) == 1 or symbol in miscellaneous:
|
||||
result.add(tokens[0])
|
||||
|
||||
return tuple(sorted(result))
|
||||
|
||||
def _get_single_token_id(self, text) -> int:
|
||||
tokens = self.tokenizer.encode(text)
|
||||
assert len(tokens) == 1, f"{text} is not encoded as a single token"
|
||||
return tokens[0]
|
||||
|
||||
|
||||
@lru_cache(maxsize=None)
|
||||
def build_tokenizer(name: str = "gpt2"):
|
||||
os.environ["TOKENIZERS_PARALLELISM"] = "false"
|
||||
path = os.path.join(os.path.dirname(__file__), "assets", name)
|
||||
tokenizer = GPT2TokenizerFast.from_pretrained(path)
|
||||
|
||||
specials = [
|
||||
"<|startoftranscript|>",
|
||||
*[f"<|{lang}|>" for lang in LANGUAGES.keys()],
|
||||
"<|translate|>",
|
||||
"<|transcribe|>",
|
||||
"<|startoflm|>",
|
||||
"<|startofprev|>",
|
||||
"<|nospeech|>",
|
||||
"<|notimestamps|>",
|
||||
]
|
||||
|
||||
tokenizer.add_special_tokens(dict(additional_special_tokens=specials))
|
||||
return tokenizer
|
||||
|
||||
|
||||
@lru_cache(maxsize=None)
|
||||
def get_tokenizer(
|
||||
multilingual: bool,
|
||||
*,
|
||||
task: Optional[str] = None, # Literal["transcribe", "translate", None]
|
||||
language: Optional[str] = None,
|
||||
) -> Tokenizer:
|
||||
if language is not None:
|
||||
language = language.lower()
|
||||
if language not in LANGUAGES:
|
||||
if language in TO_LANGUAGE_CODE:
|
||||
language = TO_LANGUAGE_CODE[language]
|
||||
else:
|
||||
raise ValueError(f"Unsupported language: {language}")
|
||||
|
||||
if multilingual:
|
||||
tokenizer_name = "multilingual"
|
||||
task = task or "transcribe"
|
||||
language = language or "en"
|
||||
else:
|
||||
tokenizer_name = "gpt2"
|
||||
task = None
|
||||
language = None
|
||||
|
||||
tokenizer = build_tokenizer(name=tokenizer_name)
|
||||
all_special_ids: List[int] = tokenizer.all_special_ids
|
||||
sot: int = all_special_ids[1]
|
||||
translate: int = all_special_ids[-6]
|
||||
transcribe: int = all_special_ids[-5]
|
||||
|
||||
langs = tuple(LANGUAGES.keys())
|
||||
sot_sequence = [sot]
|
||||
if language is not None:
|
||||
sot_sequence.append(sot + 1 + langs.index(language))
|
||||
if task is not None:
|
||||
sot_sequence.append(transcribe if task == "transcribe" else translate)
|
||||
|
||||
return Tokenizer(tokenizer=tokenizer, language=language, sot_sequence=tuple(sot_sequence))
|
||||
207
musetalk/whisper/whisper/transcribe.py
Normal file
207
musetalk/whisper/whisper/transcribe.py
Normal file
@@ -0,0 +1,207 @@
|
||||
import argparse
|
||||
import os
|
||||
import warnings
|
||||
from typing import List, Optional, Tuple, Union, TYPE_CHECKING
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
import tqdm
|
||||
|
||||
from .audio import SAMPLE_RATE, N_FRAMES, HOP_LENGTH, pad_or_trim, log_mel_spectrogram
|
||||
from .decoding import DecodingOptions, DecodingResult
|
||||
from .tokenizer import LANGUAGES, TO_LANGUAGE_CODE, get_tokenizer
|
||||
from .utils import exact_div, format_timestamp, optional_int, optional_float, str2bool, write_txt, write_vtt, write_srt
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from .model import Whisper
|
||||
|
||||
|
||||
def transcribe(
|
||||
model: "Whisper",
|
||||
audio: Union[str, np.ndarray, torch.Tensor],
|
||||
*,
|
||||
verbose: Optional[bool] = None,
|
||||
temperature: Union[float, Tuple[float, ...]] = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0),
|
||||
compression_ratio_threshold: Optional[float] = 2.4,
|
||||
logprob_threshold: Optional[float] = -1.0,
|
||||
no_speech_threshold: Optional[float] = 0.6,
|
||||
condition_on_previous_text: bool = True,
|
||||
force_extraction: bool = False,
|
||||
**decode_options,
|
||||
):
|
||||
"""
|
||||
Transcribe an audio file using Whisper
|
||||
|
||||
Parameters
|
||||
----------
|
||||
model: Whisper
|
||||
The Whisper model instance
|
||||
|
||||
audio: Union[str, np.ndarray, torch.Tensor]
|
||||
The path to the audio file to open, or the audio waveform
|
||||
|
||||
verbose: bool
|
||||
Whether to display the text being decoded to the console. If True, displays all the details,
|
||||
If False, displays minimal details. If None, does not display anything
|
||||
|
||||
temperature: Union[float, Tuple[float, ...]]
|
||||
Temperature for sampling. It can be a tuple of temperatures, which will be successfully used
|
||||
upon failures according to either `compression_ratio_threshold` or `logprob_threshold`.
|
||||
|
||||
compression_ratio_threshold: float
|
||||
If the gzip compression ratio is above this value, treat as failed
|
||||
|
||||
logprob_threshold: float
|
||||
If the average log probability over sampled tokens is below this value, treat as failed
|
||||
|
||||
no_speech_threshold: float
|
||||
If the no_speech probability is higher than this value AND the average log probability
|
||||
over sampled tokens is below `logprob_threshold`, consider the segment as silent
|
||||
|
||||
condition_on_previous_text: bool
|
||||
if True, the previous output of the model is provided as a prompt for the next window;
|
||||
disabling may make the text inconsistent across windows, but the model becomes less prone to
|
||||
getting stuck in a failure loop, such as repetition looping or timestamps going out of sync.
|
||||
|
||||
decode_options: dict
|
||||
Keyword arguments to construct `DecodingOptions` instances
|
||||
|
||||
Returns
|
||||
-------
|
||||
A dictionary containing the resulting text ("text") and segment-level details ("segments"), and
|
||||
the spoken language ("language"), which is detected when `decode_options["language"]` is None.
|
||||
"""
|
||||
dtype = torch.float16 if decode_options.get("fp16", True) else torch.float32
|
||||
if model.device == torch.device("cpu"):
|
||||
if torch.cuda.is_available():
|
||||
warnings.warn("Performing inference on CPU when CUDA is available")
|
||||
if dtype == torch.float16:
|
||||
warnings.warn("FP16 is not supported on CPU; using FP32 instead")
|
||||
dtype = torch.float32
|
||||
|
||||
if dtype == torch.float32:
|
||||
decode_options["fp16"] = False
|
||||
|
||||
mel = log_mel_spectrogram(audio)
|
||||
|
||||
all_segments = []
|
||||
def add_segment(
|
||||
*, start: float, end: float, encoder_embeddings
|
||||
):
|
||||
|
||||
all_segments.append(
|
||||
{
|
||||
"start": start,
|
||||
"end": end,
|
||||
"encoder_embeddings":encoder_embeddings,
|
||||
}
|
||||
)
|
||||
# show the progress bar when verbose is False (otherwise the transcribed text will be printed)
|
||||
num_frames = mel.shape[-1]
|
||||
seek = 0
|
||||
previous_seek_value = seek
|
||||
sample_skip = 3000 #
|
||||
with tqdm.tqdm(total=num_frames, unit='frames', disable=verbose is not False) as pbar:
|
||||
while seek < num_frames:
|
||||
# seek是开始的帧数
|
||||
end_seek = min(seek + sample_skip, num_frames)
|
||||
segment = pad_or_trim(mel[:,seek:seek+sample_skip], N_FRAMES).to(model.device).to(dtype)
|
||||
|
||||
single = segment.ndim == 2
|
||||
if single:
|
||||
segment = segment.unsqueeze(0)
|
||||
if dtype == torch.float16:
|
||||
segment = segment.half()
|
||||
audio_features, embeddings = model.encoder(segment, include_embeddings = True)
|
||||
|
||||
encoder_embeddings = embeddings
|
||||
#print(f"encoder_embeddings shape {encoder_embeddings.shape}")
|
||||
add_segment(
|
||||
start=seek,
|
||||
end=end_seek,
|
||||
#text_tokens=tokens,
|
||||
#result=result,
|
||||
encoder_embeddings=encoder_embeddings,
|
||||
)
|
||||
seek+=sample_skip
|
||||
|
||||
return dict(segments=all_segments)
|
||||
|
||||
|
||||
def cli():
|
||||
from . import available_models
|
||||
|
||||
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
|
||||
parser.add_argument("audio", nargs="+", type=str, help="audio file(s) to transcribe")
|
||||
parser.add_argument("--model", default="small", choices=available_models(), help="name of the Whisper model to use")
|
||||
parser.add_argument("--model_dir", type=str, default=None, help="the path to save model files; uses ~/.cache/whisper by default")
|
||||
parser.add_argument("--device", default="cuda" if torch.cuda.is_available() else "cpu", help="device to use for PyTorch inference")
|
||||
parser.add_argument("--output_dir", "-o", type=str, default=".", help="directory to save the outputs")
|
||||
parser.add_argument("--verbose", type=str2bool, default=True, help="whether to print out the progress and debug messages")
|
||||
|
||||
parser.add_argument("--task", type=str, default="transcribe", choices=["transcribe", "translate"], help="whether to perform X->X speech recognition ('transcribe') or X->English translation ('translate')")
|
||||
parser.add_argument("--language", type=str, default=None, choices=sorted(LANGUAGES.keys()) + sorted([k.title() for k in TO_LANGUAGE_CODE.keys()]), help="language spoken in the audio, specify None to perform language detection")
|
||||
|
||||
parser.add_argument("--temperature", type=float, default=0, help="temperature to use for sampling")
|
||||
parser.add_argument("--best_of", type=optional_int, default=5, help="number of candidates when sampling with non-zero temperature")
|
||||
parser.add_argument("--beam_size", type=optional_int, default=5, help="number of beams in beam search, only applicable when temperature is zero")
|
||||
parser.add_argument("--patience", type=float, default=None, help="optional patience value to use in beam decoding, as in https://arxiv.org/abs/2204.05424, the default (1.0) is equivalent to conventional beam search")
|
||||
parser.add_argument("--length_penalty", type=float, default=None, help="optional token length penalty coefficient (alpha) as in https://arxiv.org/abs/1609.08144, uses simple length normalization by default")
|
||||
|
||||
parser.add_argument("--suppress_tokens", type=str, default="-1", help="comma-separated list of token ids to suppress during sampling; '-1' will suppress most special characters except common punctuations")
|
||||
parser.add_argument("--initial_prompt", type=str, default=None, help="optional text to provide as a prompt for the first window.")
|
||||
parser.add_argument("--condition_on_previous_text", type=str2bool, default=True, help="if True, provide the previous output of the model as a prompt for the next window; disabling may make the text inconsistent across windows, but the model becomes less prone to getting stuck in a failure loop")
|
||||
parser.add_argument("--fp16", type=str2bool, default=True, help="whether to perform inference in fp16; True by default")
|
||||
|
||||
parser.add_argument("--temperature_increment_on_fallback", type=optional_float, default=0.2, help="temperature to increase when falling back when the decoding fails to meet either of the thresholds below")
|
||||
parser.add_argument("--compression_ratio_threshold", type=optional_float, default=2.4, help="if the gzip compression ratio is higher than this value, treat the decoding as failed")
|
||||
parser.add_argument("--logprob_threshold", type=optional_float, default=-1.0, help="if the average log probability is lower than this value, treat the decoding as failed")
|
||||
parser.add_argument("--no_speech_threshold", type=optional_float, default=0.6, help="if the probability of the <|nospeech|> token is higher than this value AND the decoding has failed due to `logprob_threshold`, consider the segment as silence")
|
||||
parser.add_argument("--threads", type=optional_int, default=0, help="number of threads used by torch for CPU inference; supercedes MKL_NUM_THREADS/OMP_NUM_THREADS")
|
||||
|
||||
args = parser.parse_args().__dict__
|
||||
model_name: str = args.pop("model")
|
||||
model_dir: str = args.pop("model_dir")
|
||||
output_dir: str = args.pop("output_dir")
|
||||
device: str = args.pop("device")
|
||||
os.makedirs(output_dir, exist_ok=True)
|
||||
|
||||
if model_name.endswith(".en") and args["language"] not in {"en", "English"}:
|
||||
if args["language"] is not None:
|
||||
warnings.warn(f"{model_name} is an English-only model but receipted '{args['language']}'; using English instead.")
|
||||
args["language"] = "en"
|
||||
|
||||
temperature = args.pop("temperature")
|
||||
temperature_increment_on_fallback = args.pop("temperature_increment_on_fallback")
|
||||
if temperature_increment_on_fallback is not None:
|
||||
temperature = tuple(np.arange(temperature, 1.0 + 1e-6, temperature_increment_on_fallback))
|
||||
else:
|
||||
temperature = [temperature]
|
||||
|
||||
threads = args.pop("threads")
|
||||
if threads > 0:
|
||||
torch.set_num_threads(threads)
|
||||
|
||||
from . import load_model
|
||||
model = load_model(model_name, device=device, download_root=model_dir)
|
||||
|
||||
for audio_path in args.pop("audio"):
|
||||
result = transcribe(model, audio_path, temperature=temperature, **args)
|
||||
|
||||
audio_basename = os.path.basename(audio_path)
|
||||
|
||||
# save TXT
|
||||
with open(os.path.join(output_dir, audio_basename + ".txt"), "w", encoding="utf-8") as txt:
|
||||
write_txt(result["segments"], file=txt)
|
||||
|
||||
# save VTT
|
||||
with open(os.path.join(output_dir, audio_basename + ".vtt"), "w", encoding="utf-8") as vtt:
|
||||
write_vtt(result["segments"], file=vtt)
|
||||
|
||||
# save SRT
|
||||
with open(os.path.join(output_dir, audio_basename + ".srt"), "w", encoding="utf-8") as srt:
|
||||
write_srt(result["segments"], file=srt)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
cli()
|
||||
87
musetalk/whisper/whisper/utils.py
Normal file
87
musetalk/whisper/whisper/utils.py
Normal file
@@ -0,0 +1,87 @@
|
||||
import zlib
|
||||
from typing import Iterator, TextIO
|
||||
|
||||
|
||||
def exact_div(x, y):
|
||||
assert x % y == 0
|
||||
return x // y
|
||||
|
||||
|
||||
def str2bool(string):
|
||||
str2val = {"True": True, "False": False}
|
||||
if string in str2val:
|
||||
return str2val[string]
|
||||
else:
|
||||
raise ValueError(f"Expected one of {set(str2val.keys())}, got {string}")
|
||||
|
||||
|
||||
def optional_int(string):
|
||||
return None if string == "None" else int(string)
|
||||
|
||||
|
||||
def optional_float(string):
|
||||
return None if string == "None" else float(string)
|
||||
|
||||
|
||||
def compression_ratio(text) -> float:
|
||||
return len(text) / len(zlib.compress(text.encode("utf-8")))
|
||||
|
||||
|
||||
def format_timestamp(seconds: float, always_include_hours: bool = False, decimal_marker: str = '.'):
|
||||
assert seconds >= 0, "non-negative timestamp expected"
|
||||
milliseconds = round(seconds * 1000.0)
|
||||
|
||||
hours = milliseconds // 3_600_000
|
||||
milliseconds -= hours * 3_600_000
|
||||
|
||||
minutes = milliseconds // 60_000
|
||||
milliseconds -= minutes * 60_000
|
||||
|
||||
seconds = milliseconds // 1_000
|
||||
milliseconds -= seconds * 1_000
|
||||
|
||||
hours_marker = f"{hours:02d}:" if always_include_hours or hours > 0 else ""
|
||||
return f"{hours_marker}{minutes:02d}:{seconds:02d}{decimal_marker}{milliseconds:03d}"
|
||||
|
||||
|
||||
def write_txt(transcript: Iterator[dict], file: TextIO):
|
||||
for segment in transcript:
|
||||
print(segment['text'].strip(), file=file, flush=True)
|
||||
|
||||
|
||||
def write_vtt(transcript: Iterator[dict], file: TextIO):
|
||||
print("WEBVTT\n", file=file)
|
||||
for segment in transcript:
|
||||
print(
|
||||
f"{format_timestamp(segment['start'])} --> {format_timestamp(segment['end'])}\n"
|
||||
f"{segment['text'].strip().replace('-->', '->')}\n",
|
||||
file=file,
|
||||
flush=True,
|
||||
)
|
||||
|
||||
|
||||
def write_srt(transcript: Iterator[dict], file: TextIO):
|
||||
"""
|
||||
Write a transcript to a file in SRT format.
|
||||
|
||||
Example usage:
|
||||
from pathlib import Path
|
||||
from whisper.utils import write_srt
|
||||
|
||||
result = transcribe(model, audio_path, temperature=temperature, **args)
|
||||
|
||||
# save SRT
|
||||
audio_basename = Path(audio_path).stem
|
||||
with open(Path(output_dir) / (audio_basename + ".srt"), "w", encoding="utf-8") as srt:
|
||||
write_srt(result["segments"], file=srt)
|
||||
"""
|
||||
for i, segment in enumerate(transcript, start=1):
|
||||
# write srt lines
|
||||
print(
|
||||
f"{i}\n"
|
||||
f"{format_timestamp(segment['start'], always_include_hours=True, decimal_marker=',')} --> "
|
||||
f"{format_timestamp(segment['end'], always_include_hours=True, decimal_marker=',')}\n"
|
||||
f"{segment['text'].strip().replace('-->', '->')}\n",
|
||||
file=file,
|
||||
flush=True,
|
||||
)
|
||||
Reference in New Issue
Block a user