additional vad utils

utils_vad_additional.py

@@ -0,0 +1,56 @@
+from utils_vad import *
+import sys
+import os
+from pathlib import Path
+sys.path.append('/home/keras/notebook/nvme_raid/adamnsandle/silero_mono/pipelines/align/bin/')
+from align_utils import load_audio_norm
+import torch
+import pandas as pd
+import numpy as np
+sys.path.append('/home/keras/notebook/nvme_raid/adamnsandle/silero_mono/utils/')
+from open_stt import soundfile_opus as sf
+
+def split_save_audio_chunks(audio_path, model_path, save_path=None, device='cpu', absolute=True, max_duration=10, adaptive=False, **kwargs):
+
+    if not save_path:
+        save_path = str(Path(audio_path).with_name('after_vad'))
+        print(f'No save path specified! Using {save_path} to save audio chunks!')
+
+    SAMPLE_RATE = 16000
+    if type(model_path) == str:
+        #print('Loading model...')
+        model = init_jit_model(model_path, device)
+    else:
+        #print('Using loaded model')
+        model = model_path
+    save_name = Path(audio_path).stem
+    audio, sr = load_audio_norm(audio_path)
+    wav = torch.tensor(audio)
+    if adaptive:
+        speech_timestamps = get_speech_ts_adaptive(wav, model, device=device, **kwargs)
+    else:
+        speech_timestamps = get_speech_ts(wav, model, device=device, **kwargs)
+
+    full_save_path = Path(save_path, save_name)
+    if not os.path.exists(full_save_path):
+        os.makedirs(full_save_path, exist_ok=True)
+
+    chunks = []
+    if not speech_timestamps:
+        return pd.DataFrame()
+    for ts in speech_timestamps:
+        start_ts = int(ts['start'])
+        end_ts = int(ts['end'])
+
+        for i in range(start_ts, end_ts, max_duration * SAMPLE_RATE):
+            new_start = i
+            new_end = min(end_ts, i + max_duration * SAMPLE_RATE)
+            duration = round((new_end - new_start) / SAMPLE_RATE, 2)
+            chunk_path = Path(full_save_path, f'{save_name}_{new_start}-{new_end}.opus')
+            chunk_path = chunk_path.absolute() if absolute else chunk_path
+            sf.write(str(chunk_path), audio[new_start: new_end], 16000, format='OGG', subtype='OPUS')
+            chunks.append({'audio_path': chunk_path,
+                        'text': '', 
+                        'duration': duration,
+                        'domain': ''})
+    return pd.DataFrame(chunks)