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| import gradio as gr | |
| from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline | |
| import torch | |
| # Set up device and data type for torch based on GPU availability | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32 | |
| print(f"Using device: {device}, torch_dtype: {torch_dtype}") | |
| # Correct the model_id if using from the Hugging Face Model Hub | |
| model_id = "distil-whisper/distil-large-v3" | |
| model = AutoModelForSpeechSeq2Seq.from_pretrained(model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True) | |
| processor = AutoProcessor.from_pretrained(model_id) | |
| model.to(device) | |
| print(f"Model and processor loaded successfully: {model_id}") | |
| def transcribe_speech(file_info): | |
| filepath = file_info['path'] | |
| sample = processor(filepath, return_tensors="pt") | |
| input_features = sample.input_features.to(device) | |
| # Check audio length to decide on chunking | |
| audio_length_seconds = sample.input_values.shape[1] / processor.feature_extractor.sampling_rate | |
| if audio_length_seconds > 30: | |
| chunk_length_s = 15 | |
| batch_size = 2 | |
| else: | |
| chunk_length_s = None | |
| batch_size = 1 | |
| # Use the model and processor directly in the pipeline function | |
| pipe = pipeline( | |
| "automatic-speech-recognition", | |
| model=model, | |
| feature_extractor=processor.feature_extractor, | |
| tokenizer=processor.tokenizer, | |
| device=device, | |
| max_new_tokens=128, | |
| chunk_length_s=chunk_length_s, | |
| batch_size=batch_size, | |
| torch_dtype=torch_dtype | |
| ) | |
| result = pipe(input_features) | |
| return result["text"] | |
| with gr.Blocks() as demo: | |
| with gr.Tab("Transcribe Audio"): | |
| with gr.Row(): | |
| audio_input = gr.Audio(label="Upload audio file or record") | |
| with gr.Row(): | |
| audio_output = gr.Textbox(label="Transcription") | |
| demo.add_callback(transcribe_speech, inputs=[audio_input], outputs=[audio_output]) | |
| demo.launch(share=True) | |