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from transformers import pipeline
import gradio as gr
asr = pipeline(task="automatic-speech-recognition",
model="openai/whisper-medium")
# Especificar el idioma de salida en espa帽ol
asr.model.config.forced_decoder_ids = asr.tokenizer.get_decoder_prompt_ids(language="spanish", task="transcribe")
demo = gr.Blocks()
def transcribe_long_form(filepath):
if filepath is None:
gr.Warning("No audio found, please retry.")
return ""
output = asr(
filepath,
max_new_tokens=256,
chunk_length_s=30,
batch_size=8,
)
return output["text"]
ner = pipeline("ner",
model="mrm8488/bert-spanish-cased-finetuned-ner",
)
def get_ner(input_text):
if input_text is None:
gr.Warning("No transcription found, please retry.")
return {"text": "", "entities": ""}
output = ner(input_text)
return {"text": input_text, "entities": output}
def main(filepath):
transcription = transcribe_long_form(filepath)
ner = get_ner(transcription)
return transcription, ner
mic_transcribe = gr.Interface(
fn=main,
inputs=gr.Audio(sources="microphone",
type="filepath"),
outputs=[gr.Textbox(label="Transcription", lines=3),
gr.HighlightedText(label="Text with entities")],
title="Transcribir audio desde grabaci贸n",
description="Transcripci贸n de audio grabado desde micr贸fono.",
allow_flagging="never")
file_transcribe = gr.Interface(
fn=main,
inputs=gr.Audio(sources="upload",
type="filepath"),
outputs=[gr.Textbox(label="Transcription", lines=3),
gr.HighlightedText(label="Text with entities")],
title="Transcribir audio desde archivo",
description="Transcripci贸n a partir de un archivo de audio.",
allow_flagging="never",
)
with demo:
gr.TabbedInterface(
[mic_transcribe,
file_transcribe],
["Transcribe Microphone",
"Transcribe Audio File"],
)
demo.launch()
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