| import gradio as gr | |
| from transformers import pipeline, Wav2Vec2ProcessorWithLM, Wav2Vec2ForCTC | |
| import os | |
| import soundfile as sf | |
| from pyannote.audio import Pipeline | |
| import torch | |
| HF_TOKEN = os.environ.get("HF_TOKEN") | |
| model_name = "Mihaj/wav2vec2-large-xls-r-300m-ruOH-alphav" | |
| bond005_model = "bond005/wav2vec2-large-ru-golos-with-lm" | |
| processor = Wav2Vec2ProcessorWithLM.from_pretrained(bond005_model) | |
| model = Wav2Vec2ForCTC.from_pretrained(model_name) | |
| pipe = pipeline("automatic-speech-recognition", model=model, tokenizer=processor, feature_extractor=processor.feature_extractor, decoder=processor.decoder) | |
| pipeline_dia = Pipeline.from_pretrained("pyannote/speaker-diarization-3.1", | |
| use_auth_token=HF_TOKEN) | |
| def transcribe(diarise, processing, audio): | |
| y, sr = sf.read(audio) | |
| print(diarise) | |
| print(processing) | |
| if diarise: | |
| dia = pipeline_dia(audio) | |
| lines = [] | |
| for line in dia.to_lab().split('\n'): | |
| if line.strip() != "": | |
| res = line.split(" ") | |
| start = int(float(res[0]) * sr) | |
| end = int(float(res[1]) * sr) | |
| label = res[2] | |
| for i in res: | |
| print(i) | |
| if processing == "LM": | |
| trans = pipe(y[start:end])["text"] | |
| else: | |
| trans = pipe(y[start:end], chunk_length_s=10, stride_length_s=(4, 2))["text"] | |
| lines.append(f"{res[0]} {res[1]} {res[2]} : {trans}") | |
| text = "\n".join(lines) | |
| else: | |
| if processing == "LM": | |
| res = pipe(y) | |
| else: | |
| res = pipe(y, chunk_length_s=10, stride_length_s=(4, 2)) | |
| text = res["text"] | |
| return text | |
| iface = gr.Interface( | |
| fn=transcribe, | |
| inputs=[gr.Checkbox(label="Diarise", info="Do you need to separate speakers?"), gr.Radio(["LM", "Striding"], label="Processing", info="You can choose Striding or Language Model processing. Striding allows processing of longer files (above 10 sec), while LM shows higher accuracy."), gr.Audio(type="filepath")], | |
| outputs="text", | |
| title="Wav2Vec2 RuOH", | |
| description=r"Realtime demo for Russian Oral History recognition using a fine-tuned Wav2Vec large model from bond005. https://huggingface.co/bond005/wav2vec2-large-ru-golos-with-lm", | |
| ) | |
| iface.launch() |