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import gradio as gr |
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from transformers import pipeline |
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from datasets import DatasetDict, Dataset, load_dataset, Audio |
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from transformers import WhisperProcessor, WhisperForConditionalGeneration |
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def transcribe(audio): |
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processor = WhisperProcessor.from_pretrained("openai/whisper-medium") |
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model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-medium") |
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ds = Dataset.from_dict({"audio": [audio]}).cast_column("audio", Audio()) |
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ds = ds.cast_column("audio", Audio(sampling_rate=16_000)) |
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input_speech = next(iter(ds))["audio"]["array"] |
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input_features = processor(input_speech, return_tensors="pt").input_features |
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forced_decoder_ids = processor.get_decoder_prompt_ids(language = "no", task = "transcribe") |
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predicted_ids = model.generate(input_features, forced_decoder_ids = forced_decoder_ids) |
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transcription = processor.batch_decode(predicted_ids, skip_special_tokens = True) |
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return transcription |
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gr.Interface( |
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title = "OpenAI Whisper ASR Gradio Norwegian Web UI", |
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fn=transcribe, |
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inputs=[ |
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gr.inputs.Audio(type="filepath") |
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], |
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outputs=[ |
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"textbox" |
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] |
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).launch() |
