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AlessandraRA
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Upload 2 files
Browse files- app.py +68 -0
- requirements.txt +5 -0
app.py
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from transformers import pipeline
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import gradio as gr
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asr = pipeline(task="automatic-speech-recognition",
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model="openai/whisper-medium")
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# Especificar el idioma de salida en español
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asr.model.config.forced_decoder_ids = asr.tokenizer.get_decoder_prompt_ids(language="spanish", task="transcribe")
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demo = gr.Blocks()
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def transcribe_long_form(filepath):
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if filepath is None:
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gr.Warning("No audio found, please retry.")
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return ""
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output = asr(
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filepath,
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max_new_tokens=256,
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chunk_length_s=30,
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batch_size=8,
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)
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return output["text"]
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ner = pipeline("ner",
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model="mrm8488/bert-spanish-cased-finetuned-ner",
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)
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def get_ner(input_text):
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if input_text is None:
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gr.Warning("No transcription found, please retry.")
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return {"text": "", "entities": ""}
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output = ner(input_text)
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return {"text": input_text, "entities": output}
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def main(filepath):
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transcription = transcribe_long_form(filepath)
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ner = get_ner(transcription)
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return transcription, ner
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mic_transcribe = gr.Interface(
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fn=main,
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inputs=gr.Audio(sources="microphone",
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type="filepath"),
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outputs=[gr.Textbox(label="Transcription", lines=3),
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gr.HighlightedText(label="Text with entities")],
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title="Transcribir audio desde grabación",
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description="Transcripción de audio grabado desde micrófono.",
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allow_flagging="never")
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file_transcribe = gr.Interface(
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fn=main,
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inputs=gr.Audio(sources="upload",
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type="filepath"),
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outputs=[gr.Textbox(label="Transcription", lines=3),
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gr.HighlightedText(label="Text with entities")],
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title="Transcribir audio desde archivo",
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description="Transcripción a partir de un archivo de audio.",
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allow_flagging="never",
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)
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with demo:
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gr.TabbedInterface(
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[mic_transcribe,
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file_transcribe],
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["Transcribe Microphone",
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"Transcribe Audio File"],
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)
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demo.launch()
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requirements.txt
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transformers==4.37.2
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torch==2.2.0
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soundfile==0.12.1
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librosa==0.10.1
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gradio==4.16.0
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