| import gradio as gr | |
| from pyannote.audio import Pipeline | |
| from datasets import load_dataset | |
| from transformers import pipeline | |
| librispeech_en = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") | |
| asr = pipeline( | |
| "automatic-speech-recognition", | |
| model="facebook/s2t-wav2vec2-large-en-de", | |
| feature_extractor="facebook/s2t-wav2vec2-large-en-de", | |
| ) | |
| def speech_to_text(audio): | |
| translation = asr(librispeech_en[0][audio]) | |
| return translation | |
| def diarization(audio): | |
| pipeline = Pipeline.from_pretrained("pyannote/speaker-segmentation") | |
| output = pipeline(audio) | |
| result = "" | |
| for turn, _, speaker in output.itertracks(yield_label=True): | |
| text_result = speech_to_text(audio) | |
| result += "{} said '{}' from {:.3f} to {:.3f}\n".format(speaker,text_result,turn.start,turn.end) | |
| return "No output" if result == "" else result | |
| 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>" | |
| app = gr.Interface(fn=diarization, | |
| inputs=gr.inputs.Audio(source="upload", type="filepath", label="Upload your audio file here:"), | |
| outputs=gr.outputs.Textbox(type="auto", label="OUTPUT"), | |
| examples=[["test_audio1.wav"]], | |
| title=title, | |
| description=description, | |
| article=article, | |
| allow_flagging=False) | |
| app.launch(enable_queue=True) |