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from transformers import pipeline | |
pipe = pipeline('automatic-speech-recognition', model='openai/whisper-small') | |
def transcribe_speech(filepath): | |
output = pipe( | |
filepath, | |
max_new_tokens = 256, | |
generate_kwargs={ | |
"task": "transcribe", | |
"language": "english", | |
}, | |
chunk_length_s = 30, | |
batch_size = 8, | |
) | |
return output["text"] | |
import gradio as gr | |
demo = gr.Blocks() | |
mic_transcribe = gr.Interface( | |
fn = transcribe_speech, | |
inputs=gr.Audio(sources = "microphone", type = "filepath"), | |
outputs = 'text', | |
) | |
file_transcribe = gr.Interface( | |
fn = transcribe_speech, | |
inputs = gr.Audio(sources = "upload", type = "filepath"), | |
outputs ='text', | |
) | |
with demo: | |
gr.TabbedInterface( | |
[mic_transcribe, file_transcribe], | |
["Transcribe Microphone", "Transcribe Audio File"], | |
) | |
demo.launch() |