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from transformers import pipeline
import gradio as gr
asr = pipeline("automatic-speech-recognition", "facebook/wav2vec2-base-960h")
classifier = pipeline("text-classification", "michellejieli/emotion_text_classifier")
def transcribe(speech, state=""):
text = asr(speech)["text"]
state += text + " "
return text, state
def speech_to_text(speech):
text = asr(speech)["text"]
return text
def text_to_sentiment(text):
return classifier(text)[0]["label"]
demo = gr.Blocks()
with demo:
microphone = gr.Audio(source="microphone", type="filepath")
audio_file = gr.Audio(type="filepath")
text = gr.Textbox()
label = gr.Label()
b0 = gr.Button("Speech From Microphone")
b1 = gr.Button("Recognize Speech")
b2 = gr.Button("Classify Sentiment")
#b0.click(transcribe, inputs=[microphone, "state"], outputs=[text, "state"], live=True)
b0.click(transcribe, inputs=[microphone], outputs=[text])
b1.click(speech_to_text, inputs=audio_file, outputs=text)
b2.click(text_to_sentiment, inputs=text, outputs=label)
gr.Markdown("""References:
1. ASR Model: https://huggingface.co/facebook/wav2vec2-base-960h
2. Sentiment: https://huggingface.co/michellejieli/emotion_text_classifier
3. ASR Lesson: https://gradio.app/real-time-speech-recognition/
4. State: https://gradio.app/interface-state/
5. Deepspeech: https://deepspeech.readthedocs.io/en/r0.9/
""")
demo.launch() |