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| import gradio as gr | |
| from transformers import pipeline | |
| from pydub import AudioSegment | |
| import wordtodigits | |
| model = pipeline("automatic-speech-recognition", | |
| "facebook/wav2vec2-base-960h") | |
| model2 = gr.Interface.load("huggingface/facebook/fastspeech2-en-ljspeech") | |
| def asr(speech): | |
| transcript = model(speech)['text'] | |
| strings = transcript.split() | |
| text = "" | |
| equation = "" | |
| symbols = {"plus":"+","minus":"-","times":"*","divide":"/"} | |
| for i in range(len(strings)): | |
| if strings[i].lower() in symbols: | |
| text = wordtodigits.convert(text) | |
| equation += text + symbols[strings[i].lower()] | |
| text="" | |
| continue | |
| text += strings[i].lower() + " " | |
| if i == len(strings)-1: | |
| text = wordtodigits.convert(text) | |
| equation += text | |
| ans = round(eval(equation),2) | |
| return transcript, equation, ans, model2(str(ans)) | |
| gr.Interface(fn=asr, | |
| #inputs = gr.inputs.Audio(source="microphone", type="filepath", optional=False, label="Please record your voice"), | |
| inputs = gr.inputs.Audio(source="upload", type="filepath", label="Upload your audio file here"), | |
| outputs = [gr.outputs.Textbox(type="str", label="Text Translation"), | |
| gr.outputs.Textbox(type="str", label="Equation"), | |
| gr.outputs.Textbox(type="str", label="Answer"), | |
| gr.outputs.Audio(type="file", label="Speech Answer")], | |
| title = "Speech Equation Solver", | |
| description = "This app will translate your speech into text, morse code, and audio using wav2vec2-base-960h", | |
| article = "Model: <a href=\"https://huggingface.co/facebook/wav2vec2-base-960h\">Wav2Vec2-Base-960h</a>" | |
| ).launch() |