update

examples/silerovad/vad.py

@@ -0,0 +1,129 @@
+#!/usr/bin/python3
+# -*- coding: utf-8 -*-
+"""
+https://pytorch.org/hub/snakers4_silero-vad_vad/
+https://github.com/snakers4/silero-vad
+"""
+import argparse
+
+from scipy.io import wavfile
+import torch
+
+from project_settings import project_path
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--wav_file",
+        default=(project_path / "data/early_media/3300999628164249998.wav").as_posix(),
+        type=str,
+    )
+    parser.add_argument(
+        "--model_name",
+        default=(project_path / "pretrained_models/silero_vad/silero_vad.jit").as_posix(),
+        type=str,
+    )
+    parser.add_argument("--threshold", default=0.5, type=float)
+    parser.add_argument("--min_speech_duration_ms", default=250, type=int)
+    parser.add_argument("--speech_pad_ms", default=30, type=int)
+    parser.add_argument("--max_speech_duration_s", default=float("inf"), type=float)
+    parser.add_argument("--window_size_samples", default=512, type=int)
+    parser.add_argument("--min_silence_duration_ms", default=100, type=int)
+
+    args = parser.parse_args()
+    return args
+
+
+def main():
+    args = get_args()
+
+    with open(args.model_name, "rb") as f:
+        model = torch.jit.load(f, map_location="cpu")
+    model.reset_states()
+
+    sample_rate, signal = wavfile.read(args.wav_file)
+    signal = signal / 32768
+    signal = torch.tensor(signal, dtype=torch.float32)
+    print(signal)
+
+    min_speech_samples = sample_rate * args.min_speech_duration_ms / 1000
+    speech_pad_samples = sample_rate * args.speech_pad_ms / 1000
+    max_speech_samples = sample_rate * args.max_speech_duration_s - args.window_size_samples - 2 * speech_pad_samples
+    min_silence_samples = sample_rate * args.min_silence_duration_ms / 1000
+    min_silence_samples_at_max_speech = sample_rate * 98 / 1000
+
+    # probs
+    speech_probs = []
+    for start in range(0, len(signal), args.window_size_samples):
+        chunk = signal[start: start + args.window_size_samples]
+        if len(chunk) < args.window_size_samples:
+            chunk = torch.nn.functional.pad(chunk, (0, int(args.window_size_samples - len(chunk))))
+
+        speech_prob = model(chunk, sample_rate).item()
+        speech_probs.append(speech_prob)
+
+    print(speech_probs)
+
+    # segments
+    triggered = False
+    speeches = list()
+    current_speech = dict()
+    neg_threshold = args.threshold - 0.15
+    temp_end = 0
+    prev_end = next_start = 0
+
+    for i, speech_prob in enumerate(speech_probs):
+        if (speech_prob >= args.threshold) and temp_end:
+            temp_end = 0
+            if next_start < prev_end:
+                next_start = args.window_size_samples * i
+
+        if (speech_prob >= args.threshold) and not triggered:
+            triggered = True
+            current_speech["start"] = args.window_size_samples * i
+            continue
+
+        if triggered and (args.window_size_samples * i) - current_speech["start"] > max_speech_samples:
+            if prev_end:
+                current_speech["end"] = prev_end
+                speeches.append(current_speech)
+                current_speech = {}
+                if next_start < prev_end:
+                    triggered = False
+                else:
+                    current_speech["start"] = next_start
+                prev_end = next_start = temp_end = 0
+            else:
+                current_speech["end"] = args.window_size_samples * i
+                speeches.append(current_speech)
+                current_speech = {}
+                prev_end = next_start = temp_end = 0
+                triggered = False
+                continue
+
+        if speech_prob < neg_threshold and triggered:
+            if not temp_end:
+                temp_end = args.window_size_samples * i
+            if ((args.window_size_samples * i) - temp_end) > min_silence_samples_at_max_speech:
+                prev_end = temp_end
+            if (args.window_size_samples * i) - temp_end < min_silence_samples:
+                continue
+            else:
+                current_speech["end"] = temp_end
+                if (current_speech["end"] - current_speech["start"]) > min_speech_samples:
+                    speeches.append(current_speech)
+                current_speech = {}
+                prev_end = next_start = temp_end = 0
+                triggered = False
+                continue
+
+    if current_speech and (args.audio_length_samples - current_speech["start"]) > min_speech_samples:
+        current_speech["end"] = args.audio_length_samples
+        speeches.append(current_speech)
+
+    return
+
+
+if __name__ == '__main__':
+    main()

main.py

@@ -105,15 +105,15 @@ def main():
                                 webrtcvad_image = gr.Image(label="image", height=300, width=720, show_label=False)
                                 webrtcvad_end_points = gr.TextArea(label="end_points", max_lines=35)
 
-                        gr.Examples(
-                            examples=webrtcvad_examples,
-                            inputs=[
-                                webrtcvad_wav, webrtcvad_agg, webrtcvad_frame_duration_ms,
-                                webrtcvad_padding_duration_ms, webrtcvad_silence_duration_threshold
-                            ],
-                            outputs=[webrtcvad_image, webrtcvad_end_points],
-                            fn=click_webrtcvad_button
-                        )
+                        # gr.Examples(
+                        #     examples=webrtcvad_examples,
+                        #     inputs=[
+                        #         webrtcvad_wav, webrtcvad_agg, webrtcvad_frame_duration_ms,
+                        #         webrtcvad_padding_duration_ms, webrtcvad_silence_duration_threshold
+                        #     ],
+                        #     outputs=[webrtcvad_image, webrtcvad_end_points],
+                        #     fn=click_webrtcvad_button
+                        # )
 
                         # click event
                         webrtcvad_button.click(

requirements.txt

@@ -4,3 +4,5 @@ wave==0.0.2
 matplotlib==3.7.4
 scipy==1.10.1
 pillow==10.2.0
+torch==2.1.2
+torchaudio==2.1.2