Audio Classification
Transformers
Safetensors
English
wav2vec2
emotion
audio
classification
music
facebook
Instructions to use prithivMLmods/Speech-Emotion-Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivMLmods/Speech-Emotion-Classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="prithivMLmods/Speech-Emotion-Classification")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("prithivMLmods/Speech-Emotion-Classification") model = AutoModelForAudioClassification.from_pretrained("prithivMLmods/Speech-Emotion-Classification") - Notebooks
- Google Colab
- Kaggle
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# Speech-Emotion-Classification
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> **Speech-Emotion-Classification** is a fine-tuned version of `facebook/wav2vec2-base-960h` for **multi-class audio classification**, specifically trained to detect **emotions** in speech. This model utilizes the `Wav2Vec2ForSequenceClassification` architecture to accurately classify speaker emotions from audio signals.
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* **Speech Emotion Analytics** – Analyze speaker emotions in call centers, interviews, or therapeutic sessions.
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* **Conversational AI Personalization** – Adjust voice assistant responses based on detected emotion.
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* **Mental Health Monitoring** – Support emotion recognition in voice-based wellness or teletherapy apps.
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* **Voice Dataset Curation** – Tag or filter speech datasets by emotion for research or model training.
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* **Media Annotation** – Automatically annotate podcasts, audiobooks, or videos with speaker emotion metadata.
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- facebook
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# Speech-Emotion-Classification
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> **Speech-Emotion-Classification** is a fine-tuned version of `facebook/wav2vec2-base-960h` for **multi-class audio classification**, specifically trained to detect **emotions** in speech. This model utilizes the `Wav2Vec2ForSequenceClassification` architecture to accurately classify speaker emotions from audio signals.
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* **Speech Emotion Analytics** – Analyze speaker emotions in call centers, interviews, or therapeutic sessions.
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* **Conversational AI Personalization** – Adjust voice assistant responses based on detected emotion.
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* **Mental Health Monitoring** – Support emotion recognition in voice-based wellness or teletherapy apps.
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* **Voice Dataset Curation** – Tag or filter speech datasets by emotion for research or model training.
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* **Media Annotation** – Automatically annotate podcasts, audiobooks, or videos with speaker emotion metadata.
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