Create whisper_cs.py

whisper_cs.py

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+import spaces
+from pydub import AudioSegment
+import os
+import torchaudio
+import torch
+import re
+from transformers import pipeline, WhisperForConditionalGeneration, WhisperProcessor, GenerationConfig
+from pyannote.audio import Pipeline as DiarizationPipeline
+import whisperx
+import whisper_timestamped as whisper_ts
+from typing import Dict
+
+device = 0 if torch.cuda.is_available() else "cpu"
+torch_dtype = torch.float32
+
+MODEL_PATH_1 = "projecte-aina/whisper-large-v3-tiny-caesar"
+MODEL_PATH_2 = "langtech-veu/whisper-timestamped-cs"
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+
+def clean_text(input_text):
+    remove_chars = ['.', ',', ';', ':', '¿', '?', '«', '»', '-', '¡', '!', '@',
+                    '*', '{', '}', '[', ']', '=', '/', '\\', '&', '#', '…']
+    output_text = ''.join(char if char not in remove_chars else ' ' for char in input_text)
+    return ' '.join(output_text.split()).lower()
+
+
+def split_stereo_channels(audio_path):
+    ext = os.path.splitext(audio_path)[1].lower()
+
+    if ext == ".wav":
+        audio = AudioSegment.from_wav(audio_path)
+    elif ext == ".mp3":
+        audio = AudioSegment.from_file(audio_path, format="mp3")
+    else:
+        raise ValueError(f"Unsupported file format: {audio_path}")
+
+    channels = audio.split_to_mono()
+    if len(channels) != 2:
+        raise ValueError(f"Audio {audio_path} does not have 2 channels.")
+
+    channels[0].export(f"temp_mono_speaker1.wav", format="wav")  # Right
+    channels[1].export(f"temp_mono_speaker2.wav", format="wav")  # Left
+
+
+def convert_to_mono(input_path):
+    audio = AudioSegment.from_file(input_path)
+    base, ext = os.path.splitext(input_path)
+    output_path = f"{base}_merged.wav"
+    print('output_path',output_path)
+    mono = audio.set_channels(1)
+    mono.export(output_path, format="wav")
+    return output_path
+
+def save_temp_audio(waveform, sample_rate, path):
+    waveform = waveform.unsqueeze(0) if waveform.dim() == 1 else waveform
+    torchaudio.save(path, waveform, sample_rate)
+
+def format_audio(audio_path):
+    input_audio, sample_rate = torchaudio.load(audio_path)
+    if input_audio.shape[0] == 2:
+        input_audio = torch.mean(input_audio, dim=0, keepdim=True)
+    resampler = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=16000)
+    input_audio = resampler(input_audio)
+    print('resampled')
+    return input_audio.squeeze(), 16000
+
+def assign_timestamps(asr_segments, audio_path):
+    waveform, sr = format_audio(audio_path)
+    total_duration = waveform.shape[-1] / sr
+
+    total_words = sum(len(seg["text"].split()) for seg in asr_segments)
+    if total_words == 0:
+        raise ValueError("Total number of words in ASR segments is zero. Cannot assign timestamps.")
+
+    avg_word_duration = total_duration / total_words
+
+    current_time = 0.0
+    for segment in asr_segments:
+        word_count = len(segment["text"].split())
+        segment_duration = word_count * avg_word_duration
+        segment["start"] = round(current_time, 3)
+        segment["end"] = round(current_time + segment_duration, 3)
+        current_time += segment_duration
+
+    return asr_segments
+
+def hf_chunks_to_whisperx_segments(chunks):
+    return [
+        {
+            "text": chunk["text"],
+            "start": chunk["timestamp"][0],
+            "end": chunk["timestamp"][1],
+        }
+        for chunk in chunks
+        if chunk["timestamp"] and isinstance(chunk["timestamp"], (list, tuple))
+    ]
+
+def align_words_to_segments(words, segments, window=5.0):
+    aligned = []
+    seg_idx = 0
+    for word in words:
+        while seg_idx < len(segments) and segments[seg_idx]["end"] < word["start"] - window:
+            seg_idx += 1
+        for j in range(seg_idx, len(segments)):
+            seg = segments[j]
+            if seg["start"] > word["end"] + window:
+                break
+            if seg["start"] <= word["start"] < seg["end"]:
+                aligned.append((word, seg))
+                break
+    return aligned
+    
+def post_process_transcription(transcription, max_repeats=2): 
+    tokens = re.findall(r'\b\w+\'?\w*\b[.,!?]?', transcription)
+
+    cleaned_tokens = []
+    repetition_count = 0
+    previous_token = None
+
+    for token in tokens:
+        reduced_token = re.sub(r"(\w{1,3})(\1{2,})", "", token)
+
+        if reduced_token == previous_token:
+            repetition_count += 1
+            if repetition_count <= max_repeats:
+                cleaned_tokens.append(reduced_token)
+        else:
+            repetition_count = 1
+            cleaned_tokens.append(reduced_token)
+
+        previous_token = reduced_token
+
+    cleaned_transcription = " ".join(cleaned_tokens)
+    cleaned_transcription = re.sub(r'\s+', ' ', cleaned_transcription).strip()
+
+    return cleaned_transcription
+
+
+def post_merge_consecutive_segments_from_text(transcription_text: str) -> str:
+    segments = re.split(r'(\[SPEAKER_\d{2}\])', transcription_text)
+    merged_transcription = ''
+    current_speaker = None
+    current_segment = []
+
+    for i in range(1, len(segments) - 1, 2):
+        speaker_tag = segments[i]
+        text = segments[i + 1].strip()
+
+        speaker = re.search(r'\d{2}', speaker_tag).group()
+
+        if speaker == current_speaker:
+            current_segment.append(text)
+        else:
+            if current_speaker is not None:
+                merged_transcription += f'[SPEAKER_{current_speaker}] {" ".join(current_segment)}\n'
+            current_speaker = speaker
+            current_segment = [text]
+
+    if current_speaker is not None:
+        merged_transcription += f'[SPEAKER_{current_speaker}] {" ".join(current_segment)}\n'
+
+    return merged_transcription.strip()
+
+def cleanup_temp_files(*file_paths):
+    for path in file_paths:
+        if path and os.path.exists(path):
+            os.remove(path)
+
+
+
+def load_whisper_model(model_path: str):
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    model = whisper_ts.load_model(model_path, device=device)
+    return model
+
+def transcribe_audio(model, audio_path: str) -> Dict:
+    try:
+        result = whisper_ts.transcribe(
+            model,
+            audio_path,
+            beam_size=5,
+            best_of=5,
+            temperature=(0.0, 0.2, 0.4, 0.6, 0.8, 1.0),
+            vad=False,  
+            detect_disfluencies=True,
+        )
+        
+        words = []
+        for segment in result.get('segments', []):
+            for word in segment.get('words', []):
+                word_text = word.get('word', '').strip()
+                if word_text.startswith(' '):
+                    word_text = word_text[1:]
+                
+                words.append({
+                    'word': word_text,
+                    'start': word.get('start', 0),
+                    'end': word.get('end', 0),
+                    'confidence': word.get('confidence', 0)
+                })
+        
+        return {
+            'audio_path': audio_path,
+            'text': result['text'].strip(),
+            'segments': result.get('segments', []),
+            'words': words,
+            'duration': result.get('duration', 0),
+            'success': True
+        }
+        
+    except Exception as e:
+        return {
+            'audio_path': audio_path,
+            'error': str(e),
+            'success': False
+        }
+        
+
+
+diarization_pipeline = DiarizationPipeline.from_pretrained("./pyannote/config.yaml")
+align_model, metadata = whisperx.load_align_model(language_code="en", device=DEVICE)
+
+asr_pipe = pipeline(
+    task="automatic-speech-recognition",
+    model=MODEL_PATH_1,
+    chunk_length_s=30,
+    device=DEVICE,
+    return_timestamps=True)
+
+def diarization(audio_path):
+    diarization_result = diarization_pipeline(audio_path) 
+    diarized_segments = list(diarization_result.itertracks(yield_label=True))
+    print('diarized_segments',diarized_segments)
+    return diarized_segments
+
+def asr(audio_path):
+    print(f"[DEBUG] Starting ASR on audio: {audio_path}")
+    asr_result = asr_pipe(audio_path, return_timestamps=True)
+    print(f"[DEBUG] Raw ASR result: {asr_result}")
+    asr_segments = hf_chunks_to_whisperx_segments(asr_result['chunks'])
+    asr_segments = assign_timestamps(asr_segments, audio_path)
+    return asr_segments
+
+def align_asr_to_diarization(asr_segments, diarized_segments, audio_path):
+    waveform, sample_rate = format_audio(audio_path)
+    
+    word_segments = whisperx.align(asr_segments, align_model, metadata, waveform, DEVICE)
+    words = word_segments['word_segments']
+
+    diarized = [{"start": segment.start,"end": segment.end,"speaker": speaker} for segment, _, speaker in diarized_segments]
+
+    aligned_pairs = align_words_to_segments(words, diarized)
+
+    output = []
+    segment_map = {}
+    for word, segment in aligned_pairs:
+        key = (segment["start"], segment["end"], segment["speaker"])
+        if key not in segment_map:
+            segment_map[key] = []
+        segment_map[key].append(word["word"])
+
+    for (start, end, speaker), words in sorted(segment_map.items()):
+        output.append(f"[{speaker}] {' '.join(words)}")
+
+    return output
+
+def generate(audio_path, use_v2):
+
+    if use_v2:
+        model = load_whisper_model(MODEL_PATH_2)
+        split_stereo_channels(audio_path)
+
+        left_channel_path = "temp_mono_speaker2.wav"
+        right_channel_path = "temp_mono_speaker1.wav"
+        
+        left_waveform, left_sr = format_audio(left_channel_path)
+        right_waveform, right_sr = format_audio(right_channel_path)
+        left_result = transcribe_audio(model, left_waveform)
+        right_result = transcribe_audio(model, right_waveform)
+
+        def get_segments(result, speaker_label):
+            segments = result.get("segments", [])
+            if not segments:
+                return []
+            return [
+                (seg.get("start", 0.0), seg.get("end", 0.0), speaker_label, post_process_transcription(seg.get("text", "").strip()))
+                for seg in segments if seg.get("text")
+            ]
+
+        left_segs = get_segments(left_result, "Speaker 1")
+        right_segs = get_segments(right_result, "Speaker 2")
+
+        merged_transcript = sorted(
+            left_segs + right_segs,
+            key=lambda x: float(x[0]) if x[0] is not None else float("inf")
+        )     
+        
+        output = ""
+        for start, end, speaker, text in merged_transcript:
+            output += f"[{speaker}]: {text}\n"        
+       
+        clean_output = output.strip()
+        
+    else: 
+        mono_audio_path = convert_to_mono(audio_path)
+        waveform, sr = format_audio(mono_audio_path)
+        tmp_full_path = "tmp_full.wav"
+        save_temp_audio(waveform, sr, tmp_full_path)
+        diarized_segments = diarization(tmp_full_path)
+        asr_segments = asr(tmp_full_path)
+        for segment in asr_segments:
+            segment["text"] = post_process_transcription(segment["text"])
+        aligned_text = align_asr_to_diarization(asr_segments, diarized_segments, tmp_full_path)
+        
+        clean_output = ""
+        for line in aligned_text:
+            clean_output += f"{line}\n"
+        clean_output = post_merge_consecutive_segments_from_text(clean_output)        
+        cleanup_temp_files(mono_audio_path,tmp_full_path)
+
+    cleanup_temp_files(
+        "temp_mono_speaker1.wav",
+        "temp_mono_speaker2.wav"
+    )
+
+    return clean_output