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| import gradio as gr | |
| import os | |
| from dotenv import load_dotenv | |
| load_dotenv() | |
| HF_API_KEY = os.getenv("HF_API_KEY") | |
| # gr.Interface.load("models/pyannote/speaker-diarization").launch() | |
| # 1. visit hf.co/pyannote/speaker-diarization and accept user conditions | |
| # 2. visit hf.co/pyannote/segmentation and accept user conditions | |
| # 3. visit hf.co/settings/tokens to create an access token | |
| # 4. instantiate pretrained speaker diarization pipeline | |
| from pyannote.audio import Pipeline | |
| pipeline = Pipeline.from_pretrained( | |
| "pyannote/speaker-diarization@2.1", | |
| use_auth_token=HF_API_KEY, | |
| ) | |
| def transcribe_audio(audio): | |
| # Perform speech-to-text on audio file | |
| # apply the pipeline to an audio file | |
| diarization = pipeline(audio) | |
| text = "" | |
| for turn, _, speaker in diarization.itertracks(yield_label=True): | |
| text = ( | |
| text | |
| + f"start={turn.start:.1f}s stop={turn.end:.1f}s speaker_{speaker}" | |
| + "\n" | |
| ) | |
| return text | |
| iface = gr.Interface( | |
| fn=transcribe_audio, inputs=gr.Audio(source="upload"), outputs="text" | |
| ) | |
| iface.launch() | |