Upload transcriber.py

transcriber.py

@@ -0,0 +1,74 @@
+import os
+import argparse
+from datetime import timedelta
+
+import librosa
+import torch
+from faster_whisper import WhisperModel
+
+
+def seconds_to_timestamp(seconds):
+    """Convert seconds to VTT timestamp format (HH:MM:SS.mmm)"""
+    t = timedelta(seconds=seconds)
+    return str(t)[:-3].rjust(11, '0').replace('.', ',')
+
+
+def write_vtt(segments, output_path):
+    with open(output_path, 'w', encoding='utf-8') as f:
+        f.write("WEBVTT\n\n")
+        for segment in segments:
+            start_ts = seconds_to_timestamp(segment.start)
+            end_ts = seconds_to_timestamp(segment.end)
+            f.write(f"{start_ts} --> {end_ts}\n{segment.text}\n\n")
+
+
+def transcribe_audio(model, audio_path, word_timestamps=True, vad_filter=True):
+    print(f"\nProcessing {audio_path}...")
+    with torch.no_grad():
+        audio_data, sr = librosa.load(audio_path, sr=None)
+        audio_data = librosa.resample(audio_data, orig_sr=sr, target_sr=16000)
+
+        segments, _ = model.transcribe(
+            audio_data,
+            language='ar',
+            word_timestamps=word_timestamps,
+            vad_filter=vad_filter
+        )
+
+        for segment in segments:
+            if segment.words:
+                for word in segment.words:
+                    print("[%.2fs -> %.2fs] %s" % (word.start, word.end, word.word))
+
+        vtt_path = os.path.splitext(audio_path)[0] + ".vtt"
+        write_vtt(segments, vtt_path)
+        print(f"VTT written to: {vtt_path}")
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Transcribe audio files using Faster-Whisper.")
+    parser.add_argument("--model_path", required=True, help="Path to the model directory or file")
+    parser.add_argument("--audio_dir", required=True, help="Directory containing audio files (wav/mp3)")
+    parser.add_argument("--word_timestamps", type=bool, default=True, help="Enable word timestamps (default: True)")
+    parser.add_argument("--vad_filter", type=bool, default=True, help="Enable VAD filtering (default: True)")
+    args = parser.parse_args()
+
+    model = WhisperModel(args.model_path)
+
+    for file in os.listdir(args.audio_dir):
+        if file.endswith(".wav") or file.endswith(".mp3"):
+            audio_path = os.path.join(args.audio_dir, file)
+            transcribe_audio(
+                model,
+                audio_path,
+                language="ar",
+                beam_size=5,
+                task="transcribe",
+                word_timestamps=args.word_timestamps,
+                vad_filter=args.vad_filter,
+                vad_parameters=dict(min_silence_duration_ms=2000)
+            )
+
+
+if __name__ == "__main__":
+    main()