mirror of
https://github.com/snakers4/silero-vad.git
synced 2026-02-05 01:49:22 +08:00
141 lines
3.4 KiB
Plaintext
141 lines
3.4 KiB
Plaintext
{
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"cells": [
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Install Dependencies"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"!pip install -q torchaudio\n",
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"SAMPLING_RATE = 16000\n",
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"import torch\n",
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"from pprint import pprint\n",
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"\n",
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"torch.set_num_threads(1)\n",
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"NUM_PROCESS=4 # set to the number of CPU cores in the machine\n",
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"NUM_COPIES=8\n",
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"# download wav files, make multiple copies\n",
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"for idx in range(NUM_COPIES):\n",
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" torch.hub.download_url_to_file('https://models.silero.ai/vad_models/en.wav', f\"en_example{idx}.wav\")\n"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Load VAD model from torch hub"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"model, utils = torch.hub.load(repo_or_dir='snakers4/silero-vad',\n",
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" model='silero_vad',\n",
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" force_reload=True,\n",
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" onnx=False)\n",
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"\n",
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"(get_speech_timestamps,\n",
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"save_audio,\n",
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"read_audio,\n",
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"VADIterator,\n",
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"collect_chunks) = utils"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Define a vad process function"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"def init_model(model):\n",
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" global vad_model\n",
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" vad_model = model\n",
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"\n",
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"def vad_process(audio_file: str):\n",
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" global vad_model\n",
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" with torch.no_grad():\n",
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" wav = read_audio(audio_file, sampling_rate=SAMPLING_RATE)\n",
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" return get_speech_timestamps(\n",
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" wav,\n",
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" vad_model,\n",
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" 0.46, # speech prob threshold\n",
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" 16000, # sample rate\n",
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" 300, # min speech duration in ms\n",
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" 20, # max speech duration in seconds\n",
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" 600, # min silence duration\n",
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" 512, # window size\n",
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" 200, # spech pad ms\n",
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" )"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Parallelization"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from concurrent.futures import ProcessPoolExecutor, as_completed\n",
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"\n",
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"futures = []\n",
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"\n",
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"with ProcessPoolExecutor(max_workers=NUM_PROCESS, initializer=init_model, initargs=(model,)) as ex:\n",
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" for i in range(NUM_COPIES):\n",
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" futures.append(ex.submit(vad_process, f\"en_example{idx}.wav\"))\n",
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"\n",
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"for finished in as_completed(futures):\n",
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" pprint(finished.result())"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "diarization",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.15"
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},
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"orig_nbformat": 4
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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