如何提取和存储自动语音识别深度学习应用程序生成的文本？

Question

该应用程序可以在拥抱脸https://huggingface.co/spaces/rowel/asr中查看。

import gradio as gr
from transformers import pipeline


model = pipeline(task="automatic-speech-recognition",
                 model="facebook/s2t-medium-librispeech-asr")
gr.Interface.from_pipeline(model,
                           title="Automatic Speech Recognition (ASR)",
                           description="Using pipeline with Facebook S2T for ASR.",
                           examples=['data/ljspeech.wav',]
                           ).launch()

我不知道文本文件是用那几行代码存储在哪里的。我想把句子文本存储在一个字符串中。

老实说，我只知道基本的python编程。我只想将它们存储到字符串变量中，并对它们执行一些操作。

Answer 1

您可以打开Interface.from_pipeline抽象，并定义自己的G电波接口。您需要定义自己的输入、输出和预测函数，从而访问模型中的文本预测。下面是一个例子。

你可以在这里测试https://huggingface.co/spaces/radames/Speech-Recognition-Example

import gradio as gr
from transformers import pipeline


model = pipeline(task="automatic-speech-recognition",
                 model="facebook/s2t-medium-librispeech-asr")


def predict_speech_to_text(audio):
    prediction = model(audio)
    # text variable contains your voice-to-text string
    text = prediction['text']
    return text


gr.Interface(fn=predict_speech_to_text,
             title="Automatic Speech Recognition (ASR)",
             inputs=gr.inputs.Audio(
                 source="microphone", type="filepath", label="Input"),
             outputs=gr.outputs.Textbox(label="Output"),
             description="Using pipeline with F acebook S2T for ASR.",
             examples=['ljspeech.wav'],
             allow_flagging='never'
             ).launch()