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Merge pull request #137 from snakers4/adamnsandle

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Adamnsandle
This commit is contained in:
Dimitrii Voronin
2021-12-17 17:50:13 +03:00
committed by GitHub
parent 5816eb08c4 7c3eb8bfb5
commit 35d8969322
10 changed files with 185 additions and 377 deletions
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46
hubconf.py
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@@ -1,7 +1,6 @@
dependencies = ['torch', 'torchaudio']
import torch
import json
from utils_vad import (init_jit_model,
get_speech_timestamps,
get_number_ts,
@@ -12,16 +11,20 @@ from utils_vad import (init_jit_model,
VADIterator,
collect_chunks,
drop_chunks,
donwload_onnx_model)
Validator,
OnnxWrapper)
def silero_vad(**kwargs):
def silero_vad(onnx=False):
"""Silero Voice Activity Detector
Returns a model with a set of utils
Please see https://github.com/snakers4/silero-vad for usage examples
"""
hub_dir = torch.hub.get_dir()
model = init_jit_model(model_path=f'{hub_dir}/snakers4_silero-vad_master/files/silero_vad.jit')
if onnx:
model = OnnxWrapper(f'{hub_dir}/snakers4_silero-vad_master/files/silero_vad.onnx')
else:
model = init_jit_model(model_path=f'{hub_dir}/snakers4_silero-vad_master/files/silero_vad.jit')
utils = (get_speech_timestamps,
save_audio,
read_audio,
@@ -31,46 +34,53 @@ def silero_vad(**kwargs):
return model, utils
def silero_number_detector(**kwargs):
def silero_number_detector(onnx=False):
"""Silero Number Detector
Returns a model with a set of utils
Please see https://github.com/snakers4/silero-vad for usage examples
"""
torch.hub.download_url_to_file('https://models.silero.ai/vad_models/number_detector.jit', 'number_detector.jit')
model = init_jit_model(model_path='number_detector.jit')
if onnx:
url = 'https://models.silero.ai/vad_models/number_detector.onnx'
else:
url = 'https://models.silero.ai/vad_models/number_detector.jit'
model = Validator(url)
utils = (get_number_ts,
save_audio,
read_audio,
collect_chunks,
drop_chunks,
donwload_onnx_model)
drop_chunks)
return model, utils
def silero_lang_detector(**kwargs):
def silero_lang_detector(onnx=False):
"""Silero Language Classifier
Returns a model with a set of utils
Please see https://github.com/snakers4/silero-vad for usage examples
"""
torch.hub.download_url_to_file('https://models.silero.ai/vad_models/number_detector.jit', 'number_detector.jit')
model = init_jit_model(model_path='number_detector.jit')
if onnx:
url = 'https://models.silero.ai/vad_models/number_detector.onnx'
else:
url = 'https://models.silero.ai/vad_models/number_detector.jit'
model = Validator(url)
utils = (get_language,
read_audio,
donwload_onnx_model)
read_audio)
return model, utils
def silero_lang_detector_95(**kwargs):
def silero_lang_detector_95(onnx=False):
"""Silero Language Classifier (95 languages)
Returns a model with a set of utils
Please see https://github.com/snakers4/silero-vad for usage examples
"""
hub_dir = torch.hub.get_dir()
torch.hub.download_url_to_file('https://models.silero.ai/vad_models/lang_classifier_95.jit', 'lang_classifier_95.jit')
model = init_jit_model(model_path='lang_classifier_95.jit')
if onnx:
url = 'https://models.silero.ai/vad_models/lang_classifier_95.onnx'
else:
url = 'https://models.silero.ai/vad_models/lang_classifier_95.jit'
model = Validator(url)
with open(f'{hub_dir}/snakers4_silero-vad_master/files/lang_dict_95.json', 'r') as f:
lang_dict = json.load(f)
@@ -78,6 +88,6 @@ def silero_lang_detector_95(**kwargs):
with open(f'{hub_dir}/snakers4_silero-vad_master/files/lang_group_dict_95.json', 'r') as f:
lang_group_dict = json.load(f)
utils = (get_language_and_group, read_audio, donwload_onnx_model)
utils = (get_language_and_group, read_audio)
return model, lang_dict, lang_group_dict, utils

425
silero-vad.ipynb
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@@ -1,21 +1,12 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "sVNOuHQQjsrp"
},
"source": [
"# PyTorch Examples"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "FpMplOCA2Fwp"
},
"source": [
"## VAD"
"#VAD"
]
},
{
@@ -25,7 +16,7 @@
"id": "62A6F_072Fwq"
},
"source": [
"### Install Dependencies"
"## Install Dependencies"
]
},
{
@@ -42,26 +33,39 @@
"# this assumes that you have a relevant version of PyTorch installed\n",
"!pip install -q torchaudio\n",
"\n",
"SAMPLE_RATE = 16000\n",
"SAMPLING_RATE = 16000\n",
"\n",
"import glob\n",
"import torch\n",
"torch.set_num_threads(1)\n",
"\n",
"from IPython.display import Audio\n",
"from pprint import pprint\n",
"\n",
"# download example\n",
"torch.hub.download_url_to_file('https://models.silero.ai/vad_models/en.wav', 'en_example.wav')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "pSifus5IilRp"
},
"outputs": [],
"source": [
"USE_ONNX = False # change this to True if you want to test onnx model\n",
"if USE_ONNX:\n",
" !pip install -q onnxruntime\n",
" \n",
"model, utils = torch.hub.load(repo_or_dir='snakers4/silero-vad',\n",
" model='silero_vad',\n",
" force_reload=True)\n",
" force_reload=True,\n",
" onnx=USE_ONNX)\n",
"\n",
"(get_speech_timestamps,\n",
" save_audio,\n",
" read_audio,\n",
" VADIterator,\n",
" collect_chunks) = utils\n",
"\n",
"files_dir = torch.hub.get_dir() + '/snakers4_silero-vad_master/files'"
" collect_chunks) = utils"
]
},
{
@@ -70,29 +74,7 @@
"id": "fXbbaUO3jsrw"
},
"source": [
"### Full Audio"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "RJRBksv39xf5"
},
"outputs": [],
"source": [
"wav = read_audio(f'{files_dir}/en.wav', sampling_rate=SAMPLE_RATE)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "tEKb0YF_9y-i"
},
"outputs": [],
"source": [
"wav"
"## Full Audio"
]
},
{
@@ -112,9 +94,9 @@
},
"outputs": [],
"source": [
"wav = read_audio(f'{files_dir}/en.wav', sampling_rate=SAMPLE_RATE)\n",
"wav = read_audio('en_example.wav', sampling_rate=SAMPLING_RATE)\n",
"# get speech timestamps from full audio file\n",
"speech_timestamps = get_speech_timestamps(wav, model, sampling_rate=SAMPLE_RATE)\n",
"speech_timestamps = get_speech_timestamps(wav, model, sampling_rate=SAMPLING_RATE)\n",
"pprint(speech_timestamps)"
]
},
@@ -128,7 +110,7 @@
"source": [
"# merge all speech chunks to one audio\n",
"save_audio('only_speech.wav',\n",
" collect_chunks(speech_timestamps, wav), sampling_rate=16000) \n",
" collect_chunks(speech_timestamps, wav), sampling_rate=SAMPLING_RATE) \n",
"Audio('only_speech.wav')"
]
},
@@ -138,7 +120,7 @@
"id": "iDKQbVr8jsry"
},
"source": [
"### Stream imitation example"
"## Stream imitation example"
]
},
{
@@ -152,7 +134,7 @@
"## using VADIterator class\n",
"\n",
"vad_iterator = VADIterator(model)\n",
"wav = read_audio(f'{files_dir}/en.wav', sampling_rate=SAMPLE_RATE)\n",
"wav = read_audio(f'en_example.wav', sampling_rate=SAMPLING_RATE)\n",
"\n",
"window_size_samples = 1536 # number of samples in a single audio chunk\n",
"for i in range(0, len(wav), window_size_samples):\n",
@@ -172,14 +154,15 @@
"source": [
"## just probabilities\n",
"\n",
"wav = read_audio(f'{files_dir}/en.wav', sampling_rate=SAMPLE_RATE)\n",
"wav = read_audio('en_example.wav', sampling_rate=SAMPLING_RATE)\n",
"speech_probs = []\n",
"window_size_samples = 1536\n",
"for i in range(0, len(wav), window_size_samples):\n",
" speech_prob = model(wav[i: i+ window_size_samples], SAMPLE_RATE).item()\n",
" speech_prob = model(wav[i: i+ window_size_samples], SAMPLING_RATE).item()\n",
" speech_probs.append(speech_prob)\n",
"vad_iterator.reset_states() # reset model states after each audio\n",
"\n",
"pprint(speech_probs[:100])"
"print(speech_probs[:10]) # first 10 chunks predicts"
]
},
{
@@ -189,7 +172,7 @@
"id": "36jY0niD2Fww"
},
"source": [
"## Number detector"
"# Number detector"
]
},
{
@@ -200,7 +183,7 @@
"id": "scd1DlS42Fwx"
},
"source": [
"### Install Dependencies"
"## Install Dependencies"
]
},
{
@@ -215,27 +198,41 @@
"#@title Install and Import Dependencies\n",
"\n",
"# this assumes that you have a relevant version of PyTorch installed\n",
"!pip install -q torchaudio soundfile\n",
"!pip install -q torchaudio\n",
"\n",
"SAMPLING_RATE = 16000\n",
"\n",
"import glob\n",
"import torch\n",
"torch.set_num_threads(1)\n",
"\n",
"from IPython.display import Audio\n",
"from pprint import pprint\n",
"\n",
"# download example\n",
"torch.hub.download_url_to_file('https://models.silero.ai/vad_models/en_num.wav', 'en_number_example.wav')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "dPwCFHmFycUF"
},
"outputs": [],
"source": [
"USE_ONNX = False # change this to True if you want to test onnx model\n",
"if USE_ONNX:\n",
" !pip install -q onnxruntime\n",
" \n",
"model, utils = torch.hub.load(repo_or_dir='snakers4/silero-vad',\n",
" model='silero_number_detector',\n",
" force_reload=True)\n",
" force_reload=True,\n",
" onnx=USE_ONNX)\n",
"\n",
"(get_number_ts,\n",
" save_audio,\n",
" read_audio,\n",
" collect_chunks,\n",
" drop_chunks,\n",
" _) = utils\n",
"\n",
"files_dir = torch.hub.get_dir() + '/snakers4_silero-vad_master/files'"
" drop_chunks) = utils\n"
]
},
{
@@ -246,7 +243,7 @@
"id": "qhPa30ij2Fwy"
},
"source": [
"### Full audio"
"## Full audio"
]
},
{
@@ -258,7 +255,7 @@
},
"outputs": [],
"source": [
"wav = read_audio(f'{files_dir}/en_num.wav')\n",
"wav = read_audio('en_number_example.wav', sampling_rate=SAMPLING_RATE)\n",
"# get number timestamps from full audio file\n",
"number_timestamps = get_number_ts(wav, model)\n",
"pprint(number_timestamps)"
@@ -273,11 +270,10 @@
},
"outputs": [],
"source": [
"sample_rate = 16000\n",
"# convert ms in timestamps to samples\n",
"for timestamp in number_timestamps:\n",
" timestamp['start'] = int(timestamp['start'] * sample_rate / 1000)\n",
" timestamp['end'] = int(timestamp['end'] * sample_rate / 1000)"
" timestamp['start'] = int(timestamp['start'] * SAMPLING_RATE / 1000)\n",
" timestamp['end'] = int(timestamp['end'] * SAMPLING_RATE / 1000)"
]
},
{
@@ -291,7 +287,7 @@
"source": [
"# merge all number chunks to one audio\n",
"save_audio('only_numbers.wav',\n",
" collect_chunks(number_timestamps, wav), sample_rate) \n",
" collect_chunks(number_timestamps, wav), SAMPLING_RATE) \n",
"Audio('only_numbers.wav')"
]
},
@@ -306,7 +302,7 @@
"source": [
"# drop all number chunks from audio\n",
"save_audio('no_numbers.wav',\n",
" drop_chunks(number_timestamps, wav), sample_rate) \n",
" drop_chunks(number_timestamps, wav), SAMPLING_RATE) \n",
"Audio('no_numbers.wav')"
]
},
@@ -317,7 +313,7 @@
"id": "PnKtJKbq2Fwz"
},
"source": [
"## Language detector"
"# Language detector"
]
},
{
@@ -328,7 +324,7 @@
"id": "F5cAmMbP2Fwz"
},
"source": [
"### Install Dependencies"
"## Install Dependencies"
]
},
{
@@ -343,24 +339,37 @@
"#@title Install and Import Dependencies\n",
"\n",
"# this assumes that you have a relevant version of PyTorch installed\n",
"!pip install -q torchaudio soundfile\n",
"!pip install -q torchaudio\n",
"\n",
"SAMPLING_RATE = 16000\n",
"\n",
"import glob\n",
"import torch\n",
"torch.set_num_threads(1)\n",
"\n",
"from IPython.display import Audio\n",
"from pprint import pprint\n",
"\n",
"# download example\n",
"torch.hub.download_url_to_file('https://models.silero.ai/vad_models/en.wav', 'en_example.wav')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "JfRKDZiRztFe"
},
"outputs": [],
"source": [
"USE_ONNX = False # change this to True if you want to test onnx model\n",
"if USE_ONNX:\n",
" !pip install -q onnxruntime\n",
" \n",
"model, utils = torch.hub.load(repo_or_dir='snakers4/silero-vad',\n",
" model='silero_lang_detector',\n",
" force_reload=True)\n",
" force_reload=True,\n",
" onnx=USE_ONNX)\n",
"\n",
"(get_language,\n",
" read_audio,\n",
" _) = utils\n",
"\n",
"files_dir = torch.hub.get_dir() + '/snakers4_silero-vad_master/files'"
"get_language, read_audio = utils"
]
},
{
@@ -371,7 +380,7 @@
"id": "iC696eMX2Fwz"
},
"source": [
"### Full audio"
"## Full audio"
]
},
{
@@ -383,268 +392,10 @@
},
"outputs": [],
"source": [
"wav = read_audio(f'{files_dir}/en.wav')\n",
"wav = read_audio('en_example.wav', sampling_rate=SAMPLING_RATE)\n",
"lang = get_language(wav, model)\n",
"print(lang)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "57avIBd6jsrz"
},
"source": [
"# ONNX Example"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "hEhnfORV2Fw0"
},
"source": [
"## VAD"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Cy7y-NAyALSe"
},
"source": [
"**TO BE DONE**"
]
},
{
"cell_type": "markdown",
"metadata": {
"heading_collapsed": true,
"id": "7QMvUvpg2Fw4"
},
"source": [
"## Number detector"
]
},
{
"cell_type": "markdown",
"metadata": {
"heading_collapsed": true,
"hidden": true,
"id": "tBPDkpHr2Fw4"
},
"source": [
"### Install Dependencies"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hidden": true,
"id": "PdjGd56R2Fw5"
},
"outputs": [],
"source": [
"#@title Install and Import Dependencies\n",
"\n",
"# this assumes that you have a relevant version of PyTorch installed\n",
"!pip install -q torchaudio soundfile onnxruntime\n",
"\n",
"import glob\n",
"import torch\n",
"import onnxruntime\n",
"from pprint import pprint\n",
"\n",
"from IPython.display import Audio\n",
"\n",
"_, utils = torch.hub.load(repo_or_dir='snakers4/silero-vad',\n",
" model='silero_number_detector',\n",
" force_reload=True)\n",
"\n",
"(get_number_ts,\n",
" save_audio,\n",
" read_audio,\n",
" collect_chunks,\n",
" drop_chunks,\n",
" donwload_onnx_model) = utils\n",
"\n",
"files_dir = torch.hub.get_dir() + '/snakers4_silero-vad_master/files'\n",
"donwload_onnx_model('number_detector')\n",
"\n",
"def init_onnx_model(model_path: str):\n",
" return onnxruntime.InferenceSession(model_path)\n",
"\n",
"def validate_onnx(model, inputs):\n",
" with torch.no_grad():\n",
" ort_inputs = {'input': inputs.cpu().numpy()}\n",
" outs = model.run(None, ort_inputs)\n",
" outs = [torch.Tensor(x) for x in outs]\n",
" return outs"
]
},
{
"cell_type": "markdown",
"metadata": {
"heading_collapsed": true,
"hidden": true,
"id": "I9QWSFZh2Fw5"
},
"source": [
"### Full Audio"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hidden": true,
"id": "_r6QZiwu2Fw5"
},
"outputs": [],
"source": [
"model = init_onnx_model('number_detector.onnx')\n",
"wav = read_audio(f'{files_dir}/en_num.wav')\n",
"\n",
"# get number timestamps from full audio file\n",
"number_timestamps = get_number_ts(wav, model, run_function=validate_onnx)\n",
"pprint(number_timestamps)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hidden": true,
"id": "FN4aDwLV2Fw5"
},
"outputs": [],
"source": [
"sample_rate = 16000\n",
"# convert ms in timestamps to samples\n",
"for timestamp in number_timestamps:\n",
" timestamp['start'] = int(timestamp['start'] * sample_rate / 1000)\n",
" timestamp['end'] = int(timestamp['end'] * sample_rate / 1000)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hidden": true,
"id": "JnvS6WTK2Fw5"
},
"outputs": [],
"source": [
"# merge all number chunks to one audio\n",
"save_audio('only_numbers.wav',\n",
" collect_chunks(number_timestamps, wav), 16000) \n",
"Audio('only_numbers.wav')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hidden": true,
"id": "yUxOcOFG2Fw6"
},
"outputs": [],
"source": [
"# drop all number chunks from audio\n",
"save_audio('no_numbers.wav',\n",
" drop_chunks(number_timestamps, wav), 16000) \n",
"Audio('no_numbers.wav')"
]
},
{
"cell_type": "markdown",
"metadata": {
"heading_collapsed": true,
"id": "SR8Bgcd52Fw6"
},
"source": [
"## Language detector"
]
},
{
"cell_type": "markdown",
"metadata": {
"heading_collapsed": true,
"hidden": true,
"id": "PBnXPtKo2Fw6"
},
"source": [
"### Install Dependencies"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hidden": true,
"id": "iNkDWJ3H2Fw6"
},
"outputs": [],
"source": [
"#@title Install and Import Dependencies\n",
"\n",
"# this assumes that you have a relevant version of PyTorch installed\n",
"!pip install -q torchaudio soundfile onnxruntime\n",
"\n",
"import glob\n",
"import torch\n",
"import onnxruntime\n",
"from pprint import pprint\n",
"\n",
"from IPython.display import Audio\n",
"\n",
"_, utils = torch.hub.load(repo_or_dir='snakers4/silero-vad',\n",
" model='silero_lang_detector',\n",
" force_reload=True)\n",
"\n",
"(get_language,\n",
" read_audio,\n",
" donwload_onnx_model) = utils\n",
"\n",
"donwload_onnx_model('number_detector')\n",
"files_dir = torch.hub.get_dir() + '/snakers4_silero-vad_master/files'\n",
"\n",
"def init_onnx_model(model_path: str):\n",
" return onnxruntime.InferenceSession(model_path)\n",
"\n",
"def validate_onnx(model, inputs):\n",
" with torch.no_grad():\n",
" ort_inputs = {'input': inputs.cpu().numpy()}\n",
" outs = model.run(None, ort_inputs)\n",
" outs = [torch.Tensor(x) for x in outs]\n",
" return outs"
]
},
{
"cell_type": "markdown",
"metadata": {
"hidden": true,
"id": "G8N8oP4q2Fw6"
},
"source": [
"### Full Audio"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hidden": true,
"id": "WHXnh9IV2Fw6"
},
"outputs": [],
"source": [
"model = init_onnx_model('number_detector.onnx')\n",
"wav = read_audio(f'{files_dir}/en.wav')\n",
"\n",
"lang = get_language(wav, model, run_function=validate_onnx)\n",
"print(lang)"
]
}
],
"metadata": {

91
utils_vad.py
View File

@@ -5,25 +5,68 @@ import torch.nn.functional as F
import warnings
languages = ['ru', 'en', 'de', 'es']
onnx_url_dict = {
'lang_classifier_95': 'https://models.silero.ai/vad_models/lang_classifier_95.onnx',
'number_detector':'https://models.silero.ai/vad_models/number_detector.onnx'
}
def donwload_onnx_model(model_name):
class OnnxWrapper():
if model_name not in ['lang_classifier_95', 'number_detector']:
raise ValueError
def __init__(self, path):
import numpy as np
global np
import onnxruntime
self.session = onnxruntime.InferenceSession(path)
self.session.intra_op_num_threads = 1
self.session.inter_op_num_threads = 1
torch.hub.download_url_to_file(onnx_url_dict[model_name], f'{model_name}.onnx')
self.reset_states()
def reset_states(self):
self._h = np.zeros((2, 1, 64)).astype('float32')
self._c = np.zeros((2, 1, 64)).astype('float32')
def __call__(self, x, sr: int):
if x.dim() == 1:
x = x.unsqueeze(0)
if x.dim() > 2:
raise ValueError(f"Too many dimensions for input audio chunk {x.dim()}")
if x.shape[0] > 1:
raise ValueError("Onnx model does not support batching")
if sr not in [16000]:
raise ValueError(f"Supported sample rates: {[16000]}")
if sr / x.shape[1] > 31.25:
raise ValueError("Input audio chunk is too short")
ort_inputs = {'input': x.numpy(), 'h0': self._h, 'c0': self._c}
ort_outs = self.session.run(None, ort_inputs)
out, self._h, self._c = ort_outs
out = torch.tensor(out).squeeze(2)[:, 1] # make output type match JIT analog
return out
def validate(model,
inputs: torch.Tensor):
with torch.no_grad():
outs = model(inputs)
return outs
class Validator():
def __init__(self, url):
self.onnx = True if url.endswith('.onnx') else False
torch.hub.download_url_to_file(url, 'inf.model')
if self.onnx:
import onnxruntime
self.model = onnxruntime.InferenceSession('inf.model')
else:
self.model = init_jit_model(model_path='inf.model')
def __call__(self, inputs: torch.Tensor):
with torch.no_grad():
if self.onnx:
ort_inputs = {'input': inputs.cpu().numpy()}
outs = self.model.run(None, ort_inputs)
outs = [torch.Tensor(x) for x in outs]
else:
outs = self.model(inputs)
return outs
def read_audio(path: str,
@@ -215,10 +258,9 @@ def get_number_ts(wav: torch.Tensor,
model,
model_stride=8,
hop_length=160,
sample_rate=16000,
run_function=validate):
sample_rate=16000):
wav = torch.unsqueeze(wav, dim=0)
perframe_logits = run_function(model, wav)[0]
perframe_logits = model(wav)[0]
perframe_preds = torch.argmax(torch.softmax(perframe_logits, dim=1), dim=1).squeeze() # (1, num_frames_strided)
extended_preds = []
for i in perframe_preds:
@@ -245,10 +287,9 @@ def get_number_ts(wav: torch.Tensor,
def get_language(wav: torch.Tensor,
model,
run_function=validate):
model):
wav = torch.unsqueeze(wav, dim=0)
lang_logits = run_function(model, wav)[2]
lang_logits = model(wav)[2]
lang_pred = torch.argmax(torch.softmax(lang_logits, dim=1), dim=1).item() # from 0 to len(languages) - 1
assert lang_pred < len(languages)
return languages[lang_pred]
@@ -258,10 +299,9 @@ def get_language_and_group(wav: torch.Tensor,
model,
lang_dict: dict,
lang_group_dict: dict,
top_n=1,
run_function=validate):
top_n=1):
wav = torch.unsqueeze(wav, dim=0)
lang_logits, lang_group_logits = run_function(model, wav)
lang_logits, lang_group_logits = model(wav)
softm = torch.softmax(lang_logits, dim=1).squeeze()
softm_group = torch.softmax(lang_group_logits, dim=1).squeeze()
@@ -332,6 +372,13 @@ class VADIterator:
return_seconds: bool (default - False)
whether return timestamps in seconds (default - samples)
"""
if not torch.is_tensor(x):
try:
x = torch.Tensor(x)
except:
raise TypeError("Audio cannot be casted to tensor. Cast it manually")
window_size_samples = len(x[0]) if x.dim() == 2 else len(x)
self.current_sample += window_size_samples