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import gradio as gr |
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import whisper |
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from transformers import pipeline |
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import librosa |
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import soundfile as sf |
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import tempfile |
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model = whisper.load_model("base") |
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sentiment_analysis = pipeline( |
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"sentiment-analysis", |
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framework="pt", |
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model="SamLowe/roberta-base-go_emotions" |
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) |
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def analyze_sentiment(text): |
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results = sentiment_analysis(text) |
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sentiment_results = { |
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result['label']: result['score'] for result in results |
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} |
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return sentiment_results |
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def get_sentiment_emoji(sentiment): |
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emoji_mapping = { |
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"disappointment": "😞", |
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"sadness": "😢", |
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"annoyance": "😠", |
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"neutral": "😐", |
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"disapproval": "👎", |
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"realization": "😮", |
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"nervousness": "😬", |
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"approval": "👍", |
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"joy": "😄", |
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"anger": "😡", |
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"embarrassment": "😳", |
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"caring": "🤗", |
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"remorse": "😔", |
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"disgust": "🤢", |
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"grief": "😥", |
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"confusion": "😕", |
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"relief": "😌", |
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"desire": "😍", |
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"admiration": "😌", |
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"optimism": "😊", |
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"fear": "😨", |
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"love": "❤️", |
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"excitement": "🎉", |
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"curiosity": "🤔", |
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"amusement": "😄", |
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"surprise": "😲", |
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"gratitude": "🙏", |
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"pride": "🦁" |
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} |
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return emoji_mapping.get(sentiment, "") |
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def display_sentiment_results(sentiment_results, option): |
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sentiment_text = "" |
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for sentiment, score in sentiment_results.items(): |
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emoji = get_sentiment_emoji(sentiment) |
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score_percentage = score * 100 |
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if option == "Sentiment Only": |
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sentiment_text += f"{sentiment} {emoji}\n" |
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elif option == "Sentiment + Score": |
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sentiment_text += f"{sentiment} {emoji}: {score_percentage:.2f}%\n" |
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return sentiment_text |
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def load_and_resample_audio(file_path, target_sample_rate=16000): |
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audio, _ = librosa.load(file_path, sr=target_sample_rate) |
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temp_file_path = '/tmp/resampled_audio.wav' |
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sf.write(temp_file_path, audio, target_sample_rate) |
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return temp_file_path |
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def inference(audio_file_path, sentiment_option): |
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resampled_audio_path = load_and_resample_audio(audio_file_path) |
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audio = whisper.load_audio(resampled_audio_path) |
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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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lang = max(probs, key=probs.get) |
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options = whisper.DecodingOptions(fp16=False) |
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result = whisper.decode(model, mel, options) |
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sentiment_results = analyze_sentiment(result.text) |
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sentiment_output = display_sentiment_results(sentiment_results, sentiment_option) |
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return lang.upper(), result.text, sentiment_output |
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title = "🎤 Gradio UI" |
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description = "we have deployed our model on Gradio" |
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block = gr.Blocks() |
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with block: |
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gr.Markdown("# Mood Reader 🕵️♂️") |
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gr.Markdown("Your Words Whisper 🤫, But Emotions Shout 📢 – Discover What's Truly Behind Every Sentence with Mood Reader 🕵️♂️💬") |
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with gr.Column(): |
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audio = gr.Audio(label="Input Audio", type="filepath") |
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sentiment_option = gr.Radio(choices=["Sentiment Only", "Sentiment + Score"], label="Select an option") |
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transcribe_btn = gr.Button("Transcribe") |
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lang_str = gr.Textbox(label="Language") |
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text = gr.Textbox(label="Transcription") |
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sentiment_output = gr.Textbox(label="Sentiment Analysis Results") |
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transcribe_btn.click( |
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inference, |
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inputs=[audio, sentiment_option], |
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outputs=[lang_str, text, sentiment_output] |
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) |
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block.launch() |
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