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| from transformers import SeamlessM4Tv2Model, AutoProcessor | |
| import numpy as np | |
| import torch | |
| from pydub import AudioSegment | |
| # Load processor and model | |
| processor = AutoProcessor.from_pretrained("facebook/seamless-m4t-v2-large") | |
| model = SeamlessM4Tv2Model.from_pretrained("facebook/seamless-m4t-v2-large") | |
| def translate_audio(audio_file): | |
| if audio_file is None: | |
| return "No audio file detected. Please try again." | |
| try: | |
| # Set the device (use GPU if available) | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| model.to(device) | |
| # Reset audio file pointer and load audio | |
| audio = AudioSegment.from_file(audio_file, format="wav") | |
| audio = audio.set_frame_rate(16000).set_channels(1) | |
| # Convert audio to float32 NumPy array | |
| audio_array = np.array(audio.get_array_of_samples()).astype(np.float32) / 32768.0 | |
| # Process input | |
| audio_inputs = processor(audios=audio_array, sampling_rate=16000, return_tensors="pt") | |
| audio_inputs = {key: val.to(device) for key, val in audio_inputs.items()} # Ensure tensors are on the correct device | |
| # Generate translation | |
| output_tokens = model.generate(**audio_inputs, tgt_lang="eng", generate_speech=False) | |
| # Extract token IDs from the generated output | |
| token_ids = output_tokens.sequences | |
| # Decode token IDs to text | |
| translated_text_from_audio = processor.batch_decode(token_ids, skip_special_tokens=True)[0] | |
| return translated_text_from_audio | |
| except Exception as e: | |
| return f"Error during audio translation: {e}" | |