Create asr_faster_whisper.py

tools/extract/asr_faster_whisper.py

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+from pathlib import Path
+
+import torch
+from faster_whisper import WhisperModel
+
+from src.data.chapters import sec_to_hms
+
+# Set device and disable TF32 for consistent results
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+
+class ASRProcessor:
+    """
+    Automatic Speech Recognition processor using WhisperX.
+
+    Transcribes audio files and returns time-aligned transcription segments.
+    """
+
+    def __init__(self, model_name="large-v2", compute_type="float16"):
+        self.model_name = model_name
+        self.model = WhisperModel(model_name, device=device, compute_type=compute_type)
+
+    def get_asr(self, audio_file, return_duration=True):
+        assert Path(audio_file).exists(), f"File {audio_file} does not exist"
+        segments, info = self.model.transcribe(
+            audio_file, length_penalty=0.5, condition_on_previous_text=False
+        )
+
+        asr_clean = []
+        for segment in segments:
+            t = segment.text.strip()
+            s = sec_to_hms(segment.start)
+            asr_clean.append(f"{s}: {t}")
+
+        if return_duration:
+            return "\n".join(asr_clean) + "\n", info.duration
+        else:
+            return "\n".join(asr_clean) + "\n"