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| from transformers import pipeline | |
| import base64 | |
| import gradio as gr | |
| model_id = "openai/whisper-medium" # update with your model id | |
| #model_id ="openai/whisper-tiny" | |
| pipe = pipeline("automatic-speech-recognition", model=model_id) | |
| def transcribe_speech(filepath): | |
| output = pipe( | |
| filepath, | |
| max_new_tokens=256, | |
| generate_kwargs={ | |
| "task": "transcribe", | |
| "language": "spanish", | |
| }, # update with the language you've fine-tuned on | |
| chunk_length_s=30, | |
| batch_size=8, | |
| ) | |
| return output["text"] | |
| with open("Iso_Logotipo_Ceibal.png", "rb") as image_file: | |
| encoded_image = base64.b64encode(image_file.read()).decode() | |
| demo = gr.Blocks() | |
| mic_transcribe = gr.Interface( | |
| fn=transcribe_speech, | |
| inputs=gr.Audio(source="microphone", type="filepath"), | |
| outputs="textbox", | |
| ) | |
| file_transcribe = gr.Interface( | |
| fn=transcribe_speech, | |
| inputs=gr.Audio(source="upload", type="filepath"), | |
| outputs="textbox", | |
| ) | |
| with demo: | |
| gr.Markdown( | |
| """ | |
| <center> | |
| <h1> | |
| Uso de AI para transcribir audio a texto. | |
| </h1> | |
| <img src='data:image/jpg;base64,{}' width=200px> | |
| <h3> | |
| Con este espacio podrás transcribir audio a texto. | |
| </h3> | |
| </center> | |
| """.format(encoded_image)) | |
| gr.TabbedInterface( | |
| [mic_transcribe, file_transcribe], | |
| ["Transcribir desde el micrófono.", "Transcribir desde un Archivo de Audio."], | |
| ) | |
| demo.launch() |