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| import gradio as gr | |
| from transformers import Wav2Vec2Processor | |
| from transformers import AutoModelForCTC | |
| from conversationalnlp.models.wav2vec2 import Wav2Vec2Predict | |
| from conversationalnlp.models.wav2vec2 import ModelLoader | |
| from conversationalnlp.utils import * | |
| import soundfile as sf | |
| import os | |
| """ | |
| run gradio with | |
| >>python app.py | |
| """ | |
| audiosavepath = os.getcwd() | |
| pretrained_model = "codenamewei/speech-to-text" | |
| processor = Wav2Vec2Processor.from_pretrained( | |
| pretrained_model) | |
| model = AutoModelForCTC.from_pretrained( | |
| pretrained_model) | |
| modelloader = ModelLoader(model, processor) | |
| predictor = Wav2Vec2Predict(modelloader) | |
| examples = ["example1.flac", "example2.flac", "example3.flac"] | |
| def greet(audioarray): | |
| """ | |
| audio array in the following format | |
| (16000, array([ -5277184, 326400, -120320, ..., -5970432, -12745216, | |
| -6934528], dtype=int32)) | |
| <class 'tuple'> | |
| """ | |
| audioabspath = os.path.join(audiosavepath, "temp.wav") | |
| # WORKAROUND: Save to file and reread to get the array shape needed for prediction | |
| sf.write(audioabspath, audioarray[1], audioarray[0]) | |
| print(f"Audio at path {audioabspath}") | |
| predictiontexts = predictor.predictfiles([audioabspath]) | |
| outputtext = predictiontexts["predicted_text"][-1] + \ | |
| "\n" + predictiontexts["corrected_text"][-1] | |
| return outputtext | |
| demo = gr.Interface(fn=greet, inputs="audio", | |
| outputs="text", | |
| title="Speech-to-Text", | |
| examples=examples) | |
| demo.launch() # share=True) | |