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| import gradio as gr | |
| from transformers import pipeline | |
| import numpy as np | |
| import librosa | |
| from punctuators.models import PunctCapSegModelONNX | |
| transcriber = pipeline("automatic-speech-recognition", model="Oysiyl/w2v-bert-2.0-dutch-colab-CV16.0") | |
| punct_cap_model = PunctCapSegModelONNX.from_pretrained("1-800-BAD-CODE/xlm-roberta_punctuation_fullstop_truecase") | |
| def transcribe(audio): | |
| sr, y = audio | |
| y = y.astype(np.float32) | |
| y /= np.max(np.abs(y)) | |
| if sr != 16000: | |
| y = librosa.resample(y, orig_sr=sr, target_sr=16000) | |
| transcribed_text = transcriber({"sampling_rate": 16000, "raw": y})["text"] | |
| punct_cap_text = punct_cap_model.infer(texts=[transcribed_text], apply_sbd=True)[0][0] | |
| return punct_cap_text | |
| demo = gr.Interface( | |
| transcribe, | |
| gr.Audio(sources=["upload", "microphone"]), | |
| outputs="text", | |
| title="Automatic Speech Recognition for Dutch language demo", | |
| description="Click on the example below, upload audio from file or say something in microphone!", | |
| examples=[["examples/example1.wav"], ["examples/example2.wav"]], | |
| cache_examples=True | |
| ) | |
| demo.launch() |