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from transformers import pipeline, AutoModel, AutoTokenizer, AutoFeatureExtractor, Wav2Vec2ForCTC, AutoModelForSequenceClassification
import gradio as gr
model = Wav2Vec2ForCTC.from_pretrained("./asr")
tokenizer = AutoTokenizer.from_pretrained("./asr")
feature_extractor = AutoFeatureExtractor.from_pretrained("./asr")
asr = pipeline("automatic-speech-recognition",
model=model,
tokenizer=tokenizer,
feature_extractor=feature_extractor
)
model = AutoModelForSequenceClassification.from_pretrained("./tc")
tokenizer = AutoTokenizer.from_pretrained("./tc")
classifier = pipeline("text-classification", model=model, tokenizer=tokenizer)
# asr = pipeline("automatic-speech-recognition", "facebook/wav2vec2-base-960h")
# classifier = pipeline("text-classification")
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:
audio_file = gr.Audio(type="filepath")
text = gr.Textbox()
# text2 = gr.Textbox()
label = gr.Label()
b1 = gr.Button("Recognize Speech")
# b2 = gr.Button("Classify")
b1.click(speech_to_text, inputs=audio_file, outputs=text)
text.change(text_to_sentiment, inputs=text, outputs=label)
# b2.click(text_to_sentiment, inputs=text, outputs=label)
# text.change()
demo.launch() |