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import os |
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os.system("pip install git+https://github.com/openai/whisper.git") |
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import gradio as gr |
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import whisper |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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from transformers import pipeline |
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tokenizer = AutoTokenizer.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment-latest") |
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model_nlp = AutoModelForSequenceClassification.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment-latest") |
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model = whisper.load_model("small") |
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def inference_audio(audio): |
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audio = whisper.load_audio(audio) |
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audio = whisper.pad_or_trim(audio) |
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mel = whisper.log_mel_spectrogram(audio).to(model.device) |
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_, probs = model.detect_language(mel) |
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options = whisper.DecodingOptions(fp16 = False) |
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result = whisper.decode(model, mel, options) |
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return result.text |
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def inference_text(audio): |
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text =inference_audio(audio) |
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sentiment_task = pipeline("sentiment-analysis", model=model_nlp, tokenizer=tokenizer) |
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res=sentiment_task(text)[0] |
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return res['label'],res['score'] |
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audio = gr.Audio( |
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label="Input Audio", |
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show_label=False, |
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source="microphone", |
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type="filepath" |
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) |
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app=gr.Interface(title="Sentiment Audio Analysis",fn=inference_text,inputs=[audio], outputs=["text","text"]) |
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app.launch() |
