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| from transformers import pipeline | |
| import gradio as gr | |
| from pyannote.core import Annotation | |
| from pydub import AudioSegment | |
| import torchaudio | |
| from pyannote.audio import Pipeline | |
| diarization_pipe = Pipeline.from_pretrained("pyannote/speaker-diarization@2.1", | |
| use_auth_token="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") # change to "your-username/the-name-you-picked" | |
| # Load the speech-to-text model (Whisper) | |
| asr_pipe = pipeline("automatic-speech-recognition", model="SyedAunZaidi/whisper-small-hi") | |
| def transcribe_with_diarization(audio_path): | |
| # Get speaker segments using the diarization model | |
| diarization_result = diarization_pipe(audio_path) | |
| # Extract speaker segments and transcribe them using Whisper ASR | |
| transcripts = [] | |
| for track, segment,speaker in diarization_result.itertracks(yield_label=True): | |
| print(segment) | |
| print(speaker) | |
| start_time = track.start | |
| end_time = track.end | |
| print(start_time) | |
| print(end_time) | |
| label = segment # Extract the label manually | |
| waveform, sample_rate = torchaudio.load("/content/drive/MyDrive/recording.mp3", normalize=True) | |
| start_sample = int(start_time * sample_rate) | |
| end_sample = int(end_time * sample_rate) | |
| print(waveform) | |
| interval_audio = waveform[:,start_sample:end_sample] | |
| # Export the interval audio as a temporary WAV file | |
| torchaudio.save("interval_audio.wav", interval_audio,sample_rate) | |
| transcript = asr_pipe("interval_audio.wav") | |
| print(transcript) | |
| start_time = segment.start | |
| end_time = segment.end | |
| label = track[0].label() # Extract the label manually | |
| speaker_audio = audio_path + f"[{start_time:.2f},{end_time:.2f}]" | |
| transcript = asr_pipe(speaker_audio)[0]["text"] | |
| transcripts.append(transcript) | |
| # Combine the transcriptions from all speakers | |
| text = " ".join(transcripts) | |
| return text | |
| iface = gr.Interface( | |
| fn=transcribe_with_diarization, | |
| inputs=gr.Audio(source="microphone", type="filepath", filetype="wav"), | |
| outputs="text", | |
| title="Whisper Large Hindi with Speaker Diarization", | |
| description="Real-time demo for Hindi speech recognition using a fine-tuned Whisper large model with speaker diarization.", | |
| ) | |
| iface.launch() | |