import gradio as gr
import whisper
from transformers import pipeline
model = whisper.load_model("base")
sentiment_analysis = pipeline("sentiment-analysis", framework="pt", model="SamLowe/roberta-base-go_emotions")
def analyze_sentiment(text):
results = sentiment_analysis(text)
sentiment_results = {result['label']: result['score'] for result in results}
return sentiment_results
def get_sentiment_emoji(sentiment):
# Define the emojis corresponding to each sentiment
emoji_mapping = {
"disappointment": "😞",
"sadness": "😢",
"annoyance": "😠",
"neutral": "😐",
"disapproval": "👎",
"realization": "😮",
"nervousness": "😬",
"approval": "👍",
"joy": "😄",
"anger": "😡",
"embarrassment": "😳",
"caring": "🤗",
"remorse": "😔",
"disgust": "🤢",
"grief": "😥",
"confusion": "😕",
"relief": "😌",
"desire": "😍",
"admiration": "😌",
"optimism": "😊",
"fear": "😨",
"love": "❤️",
"excitement": "🎉",
"curiosity": "🤔",
"amusement": "😄",
"surprise": "😲",
"gratitude": "🙏",
"pride": "🦁"
}
return emoji_mapping.get(sentiment, "")
def display_sentiment_results(sentiment_results, option):
sentiment_text = ""
for sentiment, score in sentiment_results.items():
emoji = get_sentiment_emoji(sentiment)
if option == "Sentiment Only":
sentiment_text += f"{sentiment} {emoji}\n"
elif option == "Sentiment + Score":
sentiment_text += f"{sentiment} {emoji}: {score}\n"
return sentiment_text
def inference(audio, sentiment_option):
audio = whisper.load_audio(audio)
audio = whisper.pad_or_trim(audio)
mel = whisper.log_mel_spectrogram(audio).to(model.device)
_, probs = model.detect_language(mel)
lang = max(probs, key=probs.get)
options = whisper.DecodingOptions(fp16=False)
result = whisper.decode(model, mel, options)
sentiment_results = analyze_sentiment(result.text)
sentiment_output = display_sentiment_results(sentiment_results, sentiment_option)
return lang.upper(), result.text, sentiment_output
title = """🎤 Multilingual ASR 💬
"""
image_path = "/content/thmbnail.jpg"
description = """
💻 This demo showcases a general-purpose speech recognition model called Whisper. It is trained on a large dataset of diverse audio and supports multilingual speech recognition, speech translation, and language identification tasks.
📝 For more details, check out the [GitHub repository](https://github.com/openai/whisper).
⚙️ Components of the tool:
- Real-time multilingual speech recognition
- Language identification
- Sentiment analysis of the transcriptions
🎯 The sentiment analysis results are provided as a dictionary with different emotions and their corresponding scores.
✅ The higher the score for a specific emotion, the stronger the presence of that emotion in the transcribed text.
❓ Use the "Input Audio" option to provide an audio file or use the microphone for real-time speech recognition.
⚡️ The model will transcribe the audio and perform sentiment analysis on the transcribed text.
😃 The sentiment analysis results are displayed with emojis representing the corresponding sentiment.
"""
custom_css = """
#banner-image {
display: block;
margin-left: auto;
margin-right: auto;
}
#chat-message {
font-size: 14px;
min-height: 300px;
}
"""
block = gr.Blocks(css=custom_css)
with block:
gr.HTML(title)
with gr.Row():
with gr.Column():
gr.Image(image_path, elem_id="banner-image", show_label=False)
with gr.Column():
gr.HTML(description)
with gr.Group():
with gr.Box():
audio = gr.Audio(
label="Input Audio",
show_label=False,
source="microphone",
type="filepath"
)
sentiment_option = gr.Radio(
choices=["Sentiment Only", "Sentiment + Score"],
label="Select an option",
default="Sentiment Only"
)
btn = gr.Button("Transcribe")
lang_str = gr.Textbox(label="Language")
text = gr.Textbox(label="Transcription")
sentiment_output = gr.Textbox(label="Sentiment Analysis Results", output=True)
btn.click(inference, inputs=[audio, sentiment_option], outputs=[lang_str, text, sentiment_output])
gr.HTML('''
''')
block.launch()