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import gradio as gr
from transformers import pipeline, Wav2Vec2ProcessorWithLM
from pyannote.audio import Pipeline
from librosa import load, resample
from rpunct import RestorePuncts
asr_model = 'patrickvonplaten/wav2vec2-base-960h-4-gram'
processor = Wav2Vec2ProcessorWithLM.from_pretrained(asr_model)
asr = pipeline('automatic-speech-recognition', model=asr_model, tokenizer=processor.tokenizer, feature_extractor=processor.feature_extractor, decoder=processor.decoder)
speaker_segmentation = Pipeline.from_pretrained("pyannote/speaker-segmentation")
rpunct = RestorePuncts()
def transcribe(filepath):
speaker_output = speaker_segmentation(filepath)
speech, sampling_rate = load(filepath)
if sampling_rate != 16000:
speech = resample(speech, sampling_rate, 16000)
text = asr(speech, return_timestamps="word")
full_text = text['text'].lower()
chunks = text['chunks']
diarized_output = ""
i = 0
for turn, _, speaker in speaker_output.itertracks(yield_label=True):
diarized = ""
while i < len(chunks) and chunks[i]['timestamp'][1] <= turn.end:
diarized += chunks[i]['text'].lower() + ' '
i += 1
if diarized != "":
diarized = rpunct.punctuate(diarized)
diarized_output += "{}: ''{}'' from {:.3f}-{:.3f}\n".format(speaker,diarized,turn.start,turn.end)
return diarized_output, full_text
mic = gr.inputs.Audio(source='microphone', type='filepath', label='Speech input', optional=False)
diarized_transcript = gr.outputs.Textbox(type='auto', label='Diarized Output')
full_transcript = gr.outputs.Textbox(type='auto', label='Full Transcript')
examples = [["meeting_audio.wav"]]
iface = gr.Interface(
theme='huggingface',
description='Testing transcription',
fn=transcribe,
inputs=[mic],
outputs=[diarized_transcript, full_transcript],
examples=examples
)
iface.launch()
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