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import torch |
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import numpy as np |
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import gradio as gr |
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from datasets import load_dataset |
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from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline |
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device = "cuda:0" if torch.cuda.is_available() else "cpu" |
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torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32 |
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model_id = "openai/whisper-large-v3" |
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model = AutoModelForSpeechSeq2Seq.from_pretrained( |
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model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True |
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) |
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model.to(device) |
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processor = AutoProcessor.from_pretrained(model_id) |
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pipe = pipeline( |
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"automatic-speech-recognition", |
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model=model, |
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tokenizer=processor.tokenizer, |
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feature_extractor=processor.feature_extractor, |
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max_new_tokens=128, |
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chunk_length_s=30, |
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batch_size=16, |
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return_timestamps=True, |
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torch_dtype=torch_dtype, |
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device=device, |
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) |
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dataset = load_dataset("distil-whisper/librispeech_long", "clean", split="validation") |
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sample = dataset[0]["audio"] |
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result = pipe(sample) |
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print(result["text"]) |
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def input(audio): |
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if audio is None: |
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raise gr.Error("No audio file submitted!") |
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sr, y = audio |
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y = y.astype(np.float32) |
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y /= np.max(np.abs(y)) |
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duration = len(y) / sr |
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if duration > 20: |
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print(duration) |
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raise gr.Error("Exceed maximum limit!") |
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elif y.ndim != 1: |
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raise gr.Error("Please use mono!") |
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text = pipe({"sampling_rate": sr, "raw": y}, generate_kwargs={"task": "transcribe"}, return_timestamps=True) |
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return text["chunks"] |
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with gr.Blocks() as demo: |
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gr.Interface( |
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fn=input, |
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inputs="audio", |
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outputs="text", |
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title="Whisper Large V3: Short Audio Timestamp Transcribe", |
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description="🤗 [whisper-large-v3](https://huggingface.co/spaces/hf-audio/whisper-large-v3), Limited the audio length to 20 seconds. Please check [here](https://github.com/DUQIA/Short-Video-To-Video) for details." |
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) |
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demo.launch() |
