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Duplicate from ja-818/speech_and_text_emotion_recognition

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Co-authored-by: Julian Arango <ja-818@users.noreply.huggingface.co>

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+ *.wav filter=lfs diff=lfs merge=lfs -text
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+ name: Check file size
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+ on: # or directly `on: [push]` to run the action on every push on any branch
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+ pull_request:
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+ branches: [main]
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+
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+ # to run this workflow manually from the Actions tab
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+ workflow_dispatch:
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+ jobs:
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+ sync-to-hub:
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+ runs-on: ubuntu-latest
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+ steps:
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+ - name: Check large files
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+ uses: ActionsDesk/lfs-warning@v2.0
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+ with:
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+ filesizelimit: 10485760 # this is 10MB so we can sync to HF Spaces
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+ name: Sync to Hugging Face hub
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+ on:
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+ push:
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+ branches: [main]
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+
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+ # to run this workflow manually from the Actions tab
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+ workflow_dispatch:
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+ jobs:
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+ - uses: actions/checkout@v3
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+ with:
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+ fetch-depth: 0
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+ lfs: true
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+ - name: Push to hub
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+ env:
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+ HF_TOKEN: ${{ secrets.HF_TOKEN }}
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+ run: git push https://ja-818:$HF_TOKEN@huggingface.co/spaces/ja-818/speech_and_text_emotion_recognition main
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README.md ADDED
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+ ---
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+ title: Speech and Text Emotion Recognition
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+ emoji: 🦧
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+ colorFrom: black
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+ colorTo: black
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+ sdk: gradio
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+ sdk_version: 3.21.0
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+ app_file: app.py
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+ pinned: false
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+ duplicated_from: ja-818/speech_and_text_emotion_recognition
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+ ---
app.py ADDED
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+ import os
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+ import gradio as gr
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+ from models import infere_speech_emotion, infere_text_emotion, infere_voice2text
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+
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+ # Create a Gradio app object
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+ with gr.Blocks() as demo:
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+ gr.Markdown(
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+ '''
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+ # Speech and Text Emotion Recognition
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+ ## Determining someone's emotions can be challenging based solely on their tone or words
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+ ### This app uses both to provide a more accurate analysis of emotional expression in a single audio recording
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+ '''
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+ )
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+
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+ # Upload audio input and output fields
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+ with gr.Tab("Upload audio"):
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+ with gr.Row():
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+ upload_input = gr.Audio(label="Audio file", source="upload", type="filepath")
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+ with gr.Column():
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+ upload_output_1 = gr.Textbox(label="Text from the audio")
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+ upload_output_2 = gr.Textbox(label="Speech emotion")
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+ upload_output_3 = gr.Textbox(label="Text emotion")
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+ btn0 = gr.Button("Analyze audio")
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+ # Examples to be used as input
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+ gr.Examples(
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+ [
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+ os.path.join(os.path.dirname(__file__), "audio/a_good_dream.wav"),
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+ os.path.join(os.path.dirname(__file__), "audio/hype_in_ai.wav"),
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+ ],
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+ upload_input,
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+ label="Examples in which speech and words express different emotions:"
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+ )
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+ # Input-output logic based on button click
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+ btn0.click(fn=infere_voice2text, inputs=upload_input, outputs=upload_output_1)
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+ btn0.click(fn=infere_speech_emotion, inputs=upload_input, outputs=upload_output_2)
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+ upload_output_1.change(fn=infere_text_emotion, inputs=upload_output_1, outputs=upload_output_3)
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+
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+ # Record audio input and output fields
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+ with gr.Tab("Record audio"):
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+ with gr.Row():
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+ record_input = gr.Audio(label="Audio recording", source="microphone", type="filepath")
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+ with gr.Column():
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+ record_output_1 = gr.Textbox(label="Text from the audio")
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+ record_output_2 = gr.Textbox(label="Speech emotion")
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+ record_output_3 = gr.Textbox(label="Text emotion")
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+ btn1 = gr.Button("Analyze audio")
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+ # Input-output logic based on button click
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+ btn1.click(fn=infere_voice2text, inputs=record_input, outputs=record_output_1)
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+ btn1.click(fn=infere_speech_emotion, inputs=record_input, outputs=record_output_2)
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+ record_output_1.change(fn=infere_text_emotion, inputs=record_output_1, outputs=record_output_3)
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+
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+ demo.launch()
audio/a_good_dream.wav ADDED
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+ oid sha256:970765169ddf95678a118d45c8a77e290a4ab18be5f3f6a313670cbc54423b9d
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+ size 858676
audio/hype_in_ai.wav ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:26fef5c2e4ed3b8b7640e28a998cdac1f1f9ade7573098a35fbea7bbd33bef4d
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+ size 1329444
models.py ADDED
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+ # Import the necessary libraries
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+ from transformers import pipeline
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+
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+ # Initialize the text classification model with a pre-trained model
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+ model_text_emotion = pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base")
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+
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+ # Initialize the audio classification model with a pre-trained SER model
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+ model_speech_emotion = pipeline("audio-classification", model="aherzberg/ser_model_fixed_label")
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+
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+ # Initialize the automatic speech recognition model with a pre-trained model that is capable of converting speech to text
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+ model_voice2text = pipeline("automatic-speech-recognition", model="openai/whisper-tiny.en")
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+
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+ # A function that uses the initialized text classification model to predict the emotion of a given text input
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+ def infere_text_emotion(text):
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+ return model_text_emotion(text)[0]["label"].capitalize()
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+
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+ # A function that uses the initialized audio classification model to predict the emotion of a given speech input
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+ def infere_speech_emotion(text):
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+ # Dict that maps the speech model emotions with the text's ones
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+ emotions_dict = {"angry": "Anger", "disgust": "Disgust", "fear": "Fear", "happy": "Joy", "neutral": "Neutral", "sad": "Sadness"}
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+ inference = model_speech_emotion(text)[0]["label"]
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+ return emotions_dict[inference]
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+
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+ # A function that uses the initialized automatic speech recognition model to convert speech (as an audio file) to text
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+ def infere_voice2text(audio_file):
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+ return model_voice2text(audio_file)["text"]
requirements.txt ADDED
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+ gradio
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+ transformers
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+ torch