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| import torch | |
| from pydub import AudioSegment | |
| import soundfile as sf | |
| import os | |
| def preprocess_audio(input_audio_path, output_audio_path): | |
| """ | |
| Converts audio to 16kHz WAV format. | |
| """ | |
| audio = AudioSegment.from_file(input_audio_path) | |
| audio = audio.set_frame_rate(16000).set_channels(1) | |
| audio.export(output_audio_path, format="wav") | |
| return output_audio_path | |
| def split_audio(audio_path, chunk_length_ms=30000): | |
| """ | |
| Splits audio into chunks of specified length. | |
| """ | |
| audio = AudioSegment.from_file(audio_path) | |
| return [audio[i : i + chunk_length_ms] for i in range(0, len(audio), chunk_length_ms)] | |
| def transcribe_chunk(audio_chunk, chunk_index, whisper_models): | |
| """ | |
| Transcribes a single audio chunk using the pre-loaded Whisper model. | |
| """ | |
| temp_path = f"temp_chunk_{chunk_index}.wav" | |
| audio_chunk.export(temp_path, format="wav") | |
| audio, _ = sf.read(temp_path) | |
| inputs = whisper_models["processor"](audio, sampling_rate=16000, return_tensors="pt") | |
| input_features = inputs.input_features.to(whisper_models["device"]) | |
| predicted_ids = whisper_models["model"].generate(input_features) | |
| transcription = whisper_models["processor"].batch_decode(predicted_ids, skip_special_tokens=True)[0] | |
| os.remove(temp_path) | |
| return transcription | |
| def speech_to_text_long(audio_path, whisper_models): | |
| """ | |
| Transcribes a long audio file by splitting it into chunks. | |
| """ | |
| processed_audio_path = "processed_audio.wav" | |
| preprocess_audio(audio_path, processed_audio_path) | |
| chunks = split_audio(processed_audio_path) | |
| transcriptions = [transcribe_chunk(chunk, idx, whisper_models) for idx, chunk in enumerate(chunks)] | |
| os.remove(processed_audio_path) | |
| return " ".join(transcriptions) | |