update
Browse files- examples/silerovad/vad.py +129 -0
- main.py +9 -9
- requirements.txt +2 -0
examples/silerovad/vad.py
ADDED
@@ -0,0 +1,129 @@
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#!/usr/bin/python3
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# -*- coding: utf-8 -*-
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"""
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https://pytorch.org/hub/snakers4_silero-vad_vad/
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https://github.com/snakers4/silero-vad
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"""
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import argparse
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from scipy.io import wavfile
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import torch
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from project_settings import project_path
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def get_args():
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--wav_file",
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default=(project_path / "data/early_media/3300999628164249998.wav").as_posix(),
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type=str,
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)
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parser.add_argument(
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"--model_name",
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default=(project_path / "pretrained_models/silero_vad/silero_vad.jit").as_posix(),
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type=str,
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)
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parser.add_argument("--threshold", default=0.5, type=float)
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parser.add_argument("--min_speech_duration_ms", default=250, type=int)
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parser.add_argument("--speech_pad_ms", default=30, type=int)
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parser.add_argument("--max_speech_duration_s", default=float("inf"), type=float)
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parser.add_argument("--window_size_samples", default=512, type=int)
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parser.add_argument("--min_silence_duration_ms", default=100, type=int)
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args = parser.parse_args()
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return args
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def main():
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args = get_args()
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with open(args.model_name, "rb") as f:
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model = torch.jit.load(f, map_location="cpu")
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model.reset_states()
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sample_rate, signal = wavfile.read(args.wav_file)
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signal = signal / 32768
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signal = torch.tensor(signal, dtype=torch.float32)
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print(signal)
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min_speech_samples = sample_rate * args.min_speech_duration_ms / 1000
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speech_pad_samples = sample_rate * args.speech_pad_ms / 1000
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max_speech_samples = sample_rate * args.max_speech_duration_s - args.window_size_samples - 2 * speech_pad_samples
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min_silence_samples = sample_rate * args.min_silence_duration_ms / 1000
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min_silence_samples_at_max_speech = sample_rate * 98 / 1000
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# probs
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speech_probs = []
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for start in range(0, len(signal), args.window_size_samples):
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chunk = signal[start: start + args.window_size_samples]
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if len(chunk) < args.window_size_samples:
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chunk = torch.nn.functional.pad(chunk, (0, int(args.window_size_samples - len(chunk))))
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speech_prob = model(chunk, sample_rate).item()
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speech_probs.append(speech_prob)
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print(speech_probs)
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# segments
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triggered = False
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speeches = list()
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current_speech = dict()
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neg_threshold = args.threshold - 0.15
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temp_end = 0
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prev_end = next_start = 0
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for i, speech_prob in enumerate(speech_probs):
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if (speech_prob >= args.threshold) and temp_end:
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temp_end = 0
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if next_start < prev_end:
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next_start = args.window_size_samples * i
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if (speech_prob >= args.threshold) and not triggered:
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triggered = True
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current_speech["start"] = args.window_size_samples * i
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continue
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if triggered and (args.window_size_samples * i) - current_speech["start"] > max_speech_samples:
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if prev_end:
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current_speech["end"] = prev_end
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speeches.append(current_speech)
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current_speech = {}
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if next_start < prev_end:
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triggered = False
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else:
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current_speech["start"] = next_start
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prev_end = next_start = temp_end = 0
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else:
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current_speech["end"] = args.window_size_samples * i
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speeches.append(current_speech)
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current_speech = {}
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prev_end = next_start = temp_end = 0
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triggered = False
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continue
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if speech_prob < neg_threshold and triggered:
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if not temp_end:
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temp_end = args.window_size_samples * i
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if ((args.window_size_samples * i) - temp_end) > min_silence_samples_at_max_speech:
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prev_end = temp_end
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if (args.window_size_samples * i) - temp_end < min_silence_samples:
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continue
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else:
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current_speech["end"] = temp_end
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if (current_speech["end"] - current_speech["start"]) > min_speech_samples:
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speeches.append(current_speech)
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current_speech = {}
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prev_end = next_start = temp_end = 0
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triggered = False
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continue
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if current_speech and (args.audio_length_samples - current_speech["start"]) > min_speech_samples:
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current_speech["end"] = args.audio_length_samples
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speeches.append(current_speech)
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return
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if __name__ == '__main__':
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main()
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main.py
CHANGED
@@ -105,15 +105,15 @@ def main():
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webrtcvad_image = gr.Image(label="image", height=300, width=720, show_label=False)
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webrtcvad_end_points = gr.TextArea(label="end_points", max_lines=35)
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gr.Examples(
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)
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# click event
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webrtcvad_button.click(
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webrtcvad_image = gr.Image(label="image", height=300, width=720, show_label=False)
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webrtcvad_end_points = gr.TextArea(label="end_points", max_lines=35)
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# gr.Examples(
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# examples=webrtcvad_examples,
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# inputs=[
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# webrtcvad_wav, webrtcvad_agg, webrtcvad_frame_duration_ms,
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# webrtcvad_padding_duration_ms, webrtcvad_silence_duration_threshold
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# ],
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# outputs=[webrtcvad_image, webrtcvad_end_points],
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# fn=click_webrtcvad_button
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# )
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# click event
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webrtcvad_button.click(
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requirements.txt
CHANGED
@@ -4,3 +4,5 @@ wave==0.0.2
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matplotlib==3.7.4
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scipy==1.10.1
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pillow==10.2.0
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matplotlib==3.7.4
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scipy==1.10.1
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pillow==10.2.0
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torch==2.1.2
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torchaudio==2.1.2
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