| from transformers import pipeline | |
| asr = pipeline(task="automatic-speech-recognition", | |
| model= "distil-whisper/distil-small.en") | |
| import gradio as gr | |
| 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=4,) | |
| return output['text'] | |
| mic_transcribe = gr.Interface( | |
| fn=transcribe_long_form, | |
| inputs=gr.Audio(sources="microphone", | |
| type="filepath"), | |
| outputs=gr.Textbox(label="Transcription", | |
| lines=7), | |
| allow_flagging="never", | |
| description="Speak into the microphone or upload an audio file to transcribe it into text. This model uses a state-of-the-art speech recognition algorithm to recognize spoken words and phrases") | |
| file_transcribe = gr.Interface( | |
| fn=transcribe_long_form, | |
| inputs=gr.Audio(sources="upload", | |
| type="filepath"), | |
| outputs=gr.Textbox(label="Transcription", | |
| lines=7), | |
| allow_flagging="never", | |
| description="Speak into the microphone or upload an audio file to transcribe it into text. This model uses a state-of-the-art speech recognition algorithm to recognize spoken words and phrases") | |
| with demo: | |
| gr.TabbedInterface( | |
| [mic_transcribe, | |
| file_transcribe], | |
| ["Transcribe Microphone", | |
| "Transcribe Audio File"], | |
| title="SpeechScribe - Automatic Speech Recognition" | |
| ) | |
| demo.launch() |