Create audio_processing.py

audio_processing.py

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+import whisperx
+import torch
+import numpy as np
+from scipy.signal import resample
+from pyannote.audio import Pipeline
+import os
+from dotenv import load_dotenv
+load_dotenv()
+import logging
+import time
+from difflib import SequenceMatcher
+hf_token = os.getenv("HF_TOKEN")
+
+CHUNK_LENGTH=30
+OVERLAP=0
+import whisperx
+import torch
+import numpy as np
+
+
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+import spaces
+
+
+def preprocess_audio(audio, chunk_size=CHUNK_LENGTH*16000, overlap=OVERLAP*16000):  # 2 seconds overlap
+    chunks = []
+    for i in range(0, len(audio), chunk_size - overlap):
+        chunk = audio[i:i+chunk_size]
+        if len(chunk) < chunk_size:
+            chunk = np.pad(chunk, (0, chunk_size - len(chunk)))
+        chunks.append(chunk)
+    return chunks
+
+@spaces.GPU()
+def process_audio(audio_file, translate=False, model_size="small"):
+    start_time = time.time()
+    
+    try:
+        device = "cuda" if torch.cuda.is_available() else "cpu"
+        print(f"Using device: {device}")
+        compute_type = "int8" if torch.cuda.is_available() else "float32"
+        audio = whisperx.load_audio(audio_file)
+        model = whisperx.load_model(model_size, device, compute_type=compute_type)
+
+        diarization_pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization", use_auth_token=hf_token)
+        diarization_pipeline = diarization_pipeline.to(torch.device(device))
+
+        diarization_result = diarization_pipeline({"waveform": torch.from_numpy(audio).unsqueeze(0), "sample_rate": 16000})
+
+        chunks = preprocess_audio(audio)
+
+        language_segments = []
+        final_segments = []
+        
+        overlap_duration = OVERLAP  # 2 seconds overlap
+        for i, chunk in enumerate(chunks):
+            chunk_start_time = i * (CHUNK_LENGTH - overlap_duration)
+            chunk_end_time = chunk_start_time + CHUNK_LENGTH
+            logger.info(f"Processing chunk {i+1}/{len(chunks)}")
+            lang = model.detect_language(chunk)
+            result_transcribe = model.transcribe(chunk, language=lang)
+            if translate:
+                result_translate = model.transcribe(chunk, task="translate")
+            chunk_start_time = i * (CHUNK_LENGTH - overlap_duration)
+            for j, t_seg in enumerate(result_transcribe["segments"]):
+                segment_start = chunk_start_time + t_seg["start"]
+                segment_end = chunk_start_time + t_seg["end"]
+                # Skip segments in the overlapping region of the previous chunk
+                if i > 0 and segment_end <= chunk_start_time + overlap_duration:
+                    print(f"Skipping segment in overlap with previous chunk: {segment_start:.2f} - {segment_end:.2f}")
+                    continue
+            
+                # Skip segments in the overlapping region of the next chunk
+                if i < len(chunks) - 1 and segment_start >= chunk_end_time - overlap_duration:
+                    print(f"Skipping segment in overlap with next chunk: {segment_start:.2f} - {segment_end:.2f}")
+                    continue
+            
+                speakers = []
+                for turn, track, speaker in diarization_result.itertracks(yield_label=True):
+                    if turn.start <= segment_end and turn.end >= segment_start:
+                        speakers.append(speaker)
+            
+                segment = {
+                    "start": segment_start,
+                    "end": segment_end,
+                    "language": lang,
+                    "speaker": max(set(speakers), key=speakers.count) if speakers else "Unknown",
+                    "text": t_seg["text"],
+                }
+                
+                if translate:
+                    segment["translated"] = result_translate["segments"][j]["text"]
+            
+                final_segments.append(segment)
+
+            language_segments.append({
+                "language": lang,
+                "start": chunk_start_time,
+                "end": chunk_start_time + CHUNK_LENGTH
+            })
+            chunk_end_time = time.time()
+            logger.info(f"Chunk {i+1} processed in {chunk_end_time - chunk_start_time:.2f} seconds")
+
+        final_segments.sort(key=lambda x: x["start"])
+        merged_segments = merge_nearby_segments(final_segments)
+
+        end_time = time.time()
+        logger.info(f"Total processing time: {end_time - start_time:.2f} seconds")
+
+        return language_segments, final_segments
+    except Exception as e:
+        logger.error(f"An error occurred during audio processing: {str(e)}")
+        raise
+
+def merge_nearby_segments(segments, time_threshold=0.5, similarity_threshold=0.9):
+    merged = []
+    for segment in segments:
+        if not merged or segment['start'] - merged[-1]['end'] > time_threshold:
+            merged.append(segment)
+        else:
+            # Find the overlap
+            matcher = SequenceMatcher(None, merged[-1]['text'], segment['text'])
+            match = matcher.find_longest_match(0, len(merged[-1]['text']), 0, len(segment['text']))
+            
+            if match.size / len(segment['text']) > similarity_threshold:
+                # Merge the segments
+                merged_text = merged[-1]['text'] + segment['text'][match.b + match.size:]
+                merged_translated = merged[-1]['translated'] + segment['translated'][match.b + match.size:]
+                
+                merged[-1]['end'] = segment['end']
+                merged[-1]['text'] = merged_text
+                merged[-1]['translated'] = merged_translated
+            else:
+                # If no significant overlap, append as a new segment
+                merged.append(segment)
+    return merged
+
+def print_results(segments):
+    for segment in segments:
+        print(f"[{segment['start']:.2f}s - {segment['end']:.2f}s] ({segment['language']}) {segment['speaker']}:")
+        print(f"Original: {segment['text']}")
+        if 'translated' in segment:
+            print(f"Translated: {segment['translated']}")
+        print()