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| """ | |
| Speech Recognition Module using Whisper Large-v3 | |
| Handles audio preprocessing and transcription | |
| """ | |
| import logging | |
| import numpy as np | |
| logger = logging.getLogger(__name__) | |
| import torch | |
| from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor | |
| from pydub import AudioSegment | |
| import soundfile as sf # Add this import | |
| def transcribe_audio(audio_path): | |
| """ | |
| Convert audio file to text using Whisper ASR model | |
| Args: | |
| audio_path: Path to input audio file | |
| Returns: | |
| Transcribed English text | |
| """ | |
| logger.info(f"Starting transcription for: {audio_path}") | |
| try: | |
| # Audio conversion | |
| logger.info("Converting audio format") | |
| audio = AudioSegment.from_file(audio_path) | |
| processed_audio = audio.set_frame_rate(16000).set_channels(1) | |
| wav_path = audio_path.replace(".mp3", ".wav") | |
| processed_audio.export(wav_path, format="wav") | |
| logger.info(f"Audio converted to: {wav_path}") | |
| # Model initialization | |
| logger.info("Loading Whisper model") | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| logger.info(f"Using device: {device}") | |
| model = AutoModelForSpeechSeq2Seq.from_pretrained( | |
| "openai/whisper-large-v3", | |
| torch_dtype=torch.float32, | |
| low_cpu_mem_usage=True, | |
| use_safetensors=True | |
| ).to(device) | |
| processor = AutoProcessor.from_pretrained("openai/whisper-large-v3") | |
| logger.info("Model loaded successfully") | |
| # Processing | |
| logger.info("Processing audio input") | |
| logger.debug("Loading audio data") | |
| audio_data, sample_rate = sf.read(wav_path) | |
| audio_data = audio_data.astype(np.float32) | |
| # Increase chunk length and stride for longer transcriptions | |
| inputs = processor( | |
| audio_data, | |
| sampling_rate=16000, | |
| return_tensors="pt", | |
| # Increase chunk length to handle longer segments | |
| chunk_length_s=60, # Increased from 30 | |
| stride_length_s=10 # Increased from 5 | |
| ).to(device) | |
| # Transcription | |
| logger.info("Generating transcription") | |
| with torch.no_grad(): | |
| # Add max_length parameter to allow for longer outputs | |
| outputs = model.generate( | |
| **inputs, | |
| language="en", | |
| task="transcribe", | |
| max_length=448, # Explicitly set max output length | |
| no_repeat_ngram_size=3 # Prevent repetition in output | |
| ) | |
| result = processor.batch_decode(outputs, skip_special_tokens=True)[0] | |
| logger.info(f"transcription: %s" % result) | |
| logger.info(f"Transcription completed successfully") | |
| return result | |
| except Exception as e: | |
| logger.error(f"Transcription failed: {str(e)}", exc_info=True) | |
| raise |