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on
T4
Running
on
T4
| import torch | |
| import gradio as gr | |
| import pytube as pt | |
| from transformers import pipeline | |
| MODEL_NAME = "openai/whisper-large-v2" | |
| device = 0 if torch.cuda.is_available() else "cpu" | |
| pipe = pipeline( | |
| task="automatic-speech-recognition", | |
| model=MODEL_NAME, | |
| chunk_length_s=30, | |
| device=device, | |
| ) | |
| all_special_ids = pipe.tokenizer.all_special_ids | |
| transcribe_token_id = all_special_ids[-5] | |
| translate_token_id = all_special_ids[-6] | |
| def transcribe(microphone, state, task="transcribe"): | |
| file = microphone | |
| pipe.model.config.forced_decoder_ids = [[2, transcribe_token_id if task=="transcribe" else translate_token_id]] | |
| text = pipe(file)["text"] | |
| return state + "\n" + text, state + "\n" + text | |
| mf_transcribe = gr.Interface( | |
| fn=transcribe, | |
| inputs=[ | |
| gr.Audio(source="microphone", type="filepath", optional=True), | |
| gr.State(value="") | |
| ], | |
| outputs=[ | |
| gr.Textbox(lines=15), | |
| gr.State()] | |
| , | |
| layout="horizontal", | |
| theme="huggingface", | |
| title="Whisper Large V2: Transcribe Audio", | |
| live=True, | |
| description=( | |
| "Transcribe long-form microphone or audio inputs with the click of a button! Demo uses the" | |
| f" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe audio files" | |
| " of arbitrary length." | |
| ), | |
| allow_flagging="never", | |
| ) | |
| mf_transcribe.launch(enable_queue=True) | |