Speech Emotion Classification Onnx
ONNX INT8-quantized version of prithivMLmods/Speech-Emotion-Classification for on-device inference in macOS apps via ONNX Runtime C API.
Fonte do modelo
- Tipo
- Other
- Tamanho do arquivo
- 63,5–361 MB
- Modelo base
- prithivMLmods/Speech-Emotion-Classification
- Versão
- vRevision 5e6da67
- Atualizado
- 13 de set. de 2026
- Downloads
- 0
- Fonte
- Hugging Face
Descrição da fonte
Speech Emotion Classification — ONNX (INT8)
ONNX INT8-quantized version of prithivMLmods/Speech-Emotion-Classification for on-device inference in macOS apps via ONNX Runtime C API.
Model Details
- Architecture: Wav2Vec2ForSequenceClassification (facebook/wav2vec2-base-960h fine-tuned)
- Format: ONNX INT8 quantized
- Size: ~91 MB (INT8), ~361 MB (FP32)
- Input: Raw audio waveform (16kHz, mono), shape
[1, num_samples] - Output: 8-class emotion logits
Emotion Labels
| ID | Label | Full |
|---|---|---|
| 0 | ANG | Anger |
| 1 | CAL | Calm |
| 2 | DIS | Disgust |
| 3 | FEA | Fear |
| 4 | HAP | Happy |
| 5 | NEU | Neutral |
| 6 | SAD | Sad |
| 7 | SUR | Surprised |
Usage
On-device real-time speech emotion classification. Inference via ONNX Runtime C API.
// Swift — load and run via OnnxRuntimeWrapper
let wrapper = OnnxRuntimeWrapper()
try wrapper.load(modelPath: "model_int8.onnx")
let logits = try wrapper.run(inputName: "input_values", inputData: audioBuffer, inputShape: [1, Int64(audioBuffer.count)])
let emotionIdx = logits.firstIndex(of: logits.max()!)!
Files
model_int8.onnx— INT8 quantized model (recommended for on-device use)model.onnx— FP32 full precision modelconfig.json— Model configuration with label mappings
Attribution
Original model by prithivMLmods. Converted to ONNX by onnx-community. INT8 quantization and packaging for macOS by @smkrv.
Fontes
1 fonte
Fontes
1 fonteVerificado 13 de set.
Artefatos de modelo
8 artefatos
Artefatos de modelo
8 artefatosTrechos de fonte
2 trechos
Trechos de fonte
2 trechos--- license: apache-2.0 library_name: onnxruntime base_model: prithivMLmods/Speech-Emotion-Classification tags: - audio-classification - speech-emotion-recognition - wav2vec2 - onnx language: - en pipeline_tag: audio-classification --- # Speech Emotion Classification — ONNX (INT8) ONNX INT8-quantized version of [prithivMLmods/Speech-Emotion-Classification](https://huggingface.co/prithivMLmods/Speech-Emotion-Classification) for on-device inference in macOS apps via ONNX Runtime C API. ## Model Details - **Architecture:** Wav2Vec2ForSequenceClassification (facebook/wav2vec2-base-960h fine-tuned) - **Format:** ONNX INT8 quantized - **Size:** ~91 MB (INT8), ~361 MB (FP32) - **Input:** Raw audio waveform (16kHz, mono), shape `[1, num_samples]` - **Output:** 8-class emotion logits ### Emotion Labels | ID | Label | Full | |----|-------|------| | 0 | ANG | Anger | | 1 | CAL | Calm | | 2 | DIS | Disgust | | 3 | FEA | Fear | | 4 | HAP | Happy | | 5 | NEU | Neutral | | 6 | SAD | Sad | | 7 | SUR | Surprised | ## Usage On-device real-time speech emotion classification. Inference via ONNX Runtime C API. ```swift // Swift — load and run via OnnxRuntimeWrapper let wrapper = OnnxRuntimeWrapper() try wrapper.load(modelPath: "model_int8.onnx") let logits = try wrapper.run(inputName: "input_values", inputData: audioBuffer, inputShape: [1, Int64(audioBuffer.count)]) let emotionIdx = logits.firstIndex(of: logits.max()!)! ``` ## Files - `model_int8.onnx` — INT8 quantized model (recommended for on-device use) - `model.onnx` — FP32 full precision model - `config.json` — Model configuration with label mappings ## Attribution Original model by [prithivMLmods](https://huggingface.co/prithivMLmods). Converted to ONNX by [onnx-community](https://huggingface.co/onnx-community). INT8 quantization and packaging for macOS by [@smkrv](https://huggingface.co/smkrv).
Source context: 0 downloads · 1 likes · Pipeline audio-classification · Library onnxruntime · Repo smkrv/speech-emotion-classification-onnx
