Update audio_processing.py

audio_processing.py

@@ -2,104 +2,139 @@ import whisperx
 import torch
 import numpy as np
 from scipy.signal import resample
-import numpy as np
-import whisperx
 from pyannote.audio import Pipeline
 import os
 from dotenv import load_dotenv
-
 load_dotenv()
+import logging
+import time
+from difflib import SequenceMatcher
+import spaces
 
 hf_token = os.getenv("HF_TOKEN")
-import whisperx
-import torch
-import numpy as np
 
-import whisperx
-import torch
-import numpy as np
+CHUNK_LENGTH = 5
+OVERLAP = 2
 
-import whisperx
-import torch
-import numpy as np
-CHUNK_LENGTH= 30
-
-
-import whisperx
-import torch
-import numpy as np
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
 
-def preprocess_audio(audio, chunk_size=CHUNK_LENGTH*16000):  # 30 seconds at 16kHz
+def preprocess_audio(audio, chunk_size=CHUNK_LENGTH*16000, overlap=OVERLAP*16000):
     chunks = []
-    for i in range(0, len(audio), chunk_size):
+    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
 
-def process_audio(audio_file):
-    device = "cuda" if torch.cuda.is_available() else "cpu"
-    compute_type = "float32"
-    audio = whisperx.load_audio(audio_file)
-    model = whisperx.load_model("small", device, compute_type=compute_type)
-
-    # Initialize speaker diarization pipeline
-    diarization_pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization", use_auth_token=hf_token)
-    diarization_pipeline = diarization_pipeline.to(torch.device(device))
+@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"
+        compute_type = "float16" if device == "cuda" else "float32"
+        audio = whisperx.load_audio(audio_file)
+        model = whisperx.load_model(model_size, device, compute_type=compute_type)
 
-    # Perform diarization on the entire audio
-    diarization_result = diarization_pipeline({"waveform": torch.from_numpy(audio).unsqueeze(0), "sample_rate": 16000})
+        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})
 
-    # Preprocess audio into consistent chunks
-    chunks = preprocess_audio(audio)
+        chunks = preprocess_audio(audio)
 
-    language_segments = []
-    final_segments = []
-    
-    for i, chunk in enumerate(chunks):
-        # Detect language for this chunk
-        lang = model.detect_language(chunk)
-        
-        # Transcribe this chunk
-        result = model.transcribe(chunk, language=lang)
+        language_segments = []
+        final_segments = []
         
-        chunk_start_time = i * 5  # Each chunk is 30 seconds
-        
-        # Adjust timestamps and add language information
-        for segment in result["segments"]:
-            segment_start = chunk_start_time + segment["start"]
-            segment_end = chunk_start_time + segment["end"]
-            segment["start"] = segment_start
-            segment["end"] = segment_end
-            segment["language"] = lang
+        overlap_duration = 2  # 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
             
-            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)
-            if speakers:
-                segment["speaker"] = max(set(speakers), key=speakers.count)
-            else:
-                segment["speaker"] = "Unknown"
+                # 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)
 
-            final_segments.append(segment)
-        # Add language segment
-        language_segments.append({
-            "language": lang,
-            "start": chunk_start_time,
-            "end": chunk_start_time + 5
-        })
+            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")
 
-    return language_segments, final_segments
+        final_segments.sort(key=lambda x: x["start"])
+        merged_segments = merge_nearby_segments(final_segments)
 
-def print_results(language, language_probs, segments):
-    print(f"Detected Language: {language}")
-    print("Language Probabilities:")
-    for lang, prob in language_probs.items():
-        print(f"  {lang}: {prob:.4f}")
-    
-    print("\nTranscription:")
+        end_time = time.time()
+        logger.info(f"Total processing time: {end_time - start_time:.2f} seconds")
+
+        return language_segments, merged_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=0.7):
+    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] Speaker {segment['speaker']}: {segment['text']}")
+        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()