| import gradio as gr | |
| from pyannote.audio import Pipeline | |
| from transformers import pipeline | |
| asr = pipeline( | |
| "automatic-speech-recognition", | |
| model="facebook/wav2vec2-large-960h-lv60-self", | |
| feature_extractor="facebook/wav2vec2-large-960h-lv60-self", | |
| ) | |
| speaker_diarization = Pipeline.from_pretrained("pyannote/speaker-diarization") | |
| def diarization(audio): | |
| speaker_output = speaker_diarization(audio) | |
| text_output = asr(audio,return_timestamps="word") | |
| full_text = text_output['text'].lower() | |
| chunks = text_output['chunks'] | |
| diarized_output = "" | |
| i = 0 | |
| for turn, _, speaker in speaker_output.itertracks(yield_label=True): | |
| diarized = "" | |
| while i < len(chunks): | |
| time_index = chunks[i]['timestamp'][1] | |
| if time_index >= turn.start and time_index <= turn.end: | |
| diarized += chunks[i]['text'].lower() + ' ' | |
| if time_index >= turn.end: break | |
| i += 1 | |
| diarized_output += "{} said '{}' from {:.3f} to {:.3f}\n".format(speaker,diarized,turn.start,turn.end) | |
| return diarized_output, full_text | |
| title = "Speech Recognition with Speaker Diarization" | |
| description = "Speaker Diarization is the act of attributing parts of the audio recording to different speakers. This space aims to distinguish the speakers and apply speech-to-text from a given input audio file. Pre-trained models from Pyannote[1] for the Speaker Diarization and [2]." | |
| article = "<p style='text-align: center'><a href='https://github.com/pyannote/pyannote-audio' target='_blank'>[1] Pyannote - Speaker Diarization model</a></p>" | |
| inputs = gr.inputs.Audio(source="upload", type="filepath", label="Upload your audio file here:") | |
| outputs = [gr.outputs.Textbox(type="auto", label="Diarized Output"),gr.outputs.Textbox(type="auto",label="Full ASR Text for comparison")] | |
| examples = [["test_audio1.wav"]] | |
| app = gr.Interface(fn=diarization, | |
| inputs=inputs, | |
| outputs=outputs, | |
| examples=examples, | |
| title=title, | |
| description=description, | |
| article=article, | |
| allow_flagging=False) | |
| app.launch() |