onnx-community/xtreme_s_xlsr_300m_minds14-ONNX
By onnx-community · ↓ 3 · ♥ 0 · Updated 2026-01-21
What's in this repo
This page lists the files in onnx-community/xtreme_s_xlsr_300m_minds14-ONNX, including model.onnx, model_bnb4.onnx, model_fp16.onnx and more.
- 8 ONNX models (.onnx files)
- 13 files, including 8 .onnx, 3 .json, 1 .md
- Last updated 2026-01-21
ONNX files
| File | Size | Download |
|---|---|---|
| onnx/model.onnx | — | Download |
| onnx/model_bnb4.onnx | — | Download |
| onnx/model_fp16.onnx | — | Download |
| onnx/model_int8.onnx | — | Download |
| onnx/model_q4.onnx | — | Download |
| onnx/model_q4f16.onnx | — | Download |
| onnx/model_quantized.onnx | — | Download |
| onnx/model_uint8.onnx | — | Download |
How to use
These ONNX files can be loaded with ONNX Runtime. To inspect a model graph, upload the file to the Netron viewer. Download links point to the original files on Hugging Face.
README
xtreme_s_xlsr_300m_minds14 (ONNX)
This is an ONNX version of anton-l/xtreme_s_xlsr_300m_minds14. It was automatically converted and uploaded using this Hugging Face Space.
Usage with Transformers.js
See the pipeline documentation for audio-classification: https://huggingface.co/docs/transformers.js/api/pipelines#module_pipelines.AudioClassificationPipeline
xtreme_s_xlsr_300m_minds14
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the GOOGLE/XTREME_S - MINDS14.ALL dataset. It achieves the following results on the evaluation set:
- Accuracy: 0.9033
- Accuracy Cs-cz: 0.9164
- Accuracy De-de: 0.9477
- Accuracy En-au: 0.9235
- Accuracy En-gb: 0.9324
- Accuracy En-us: 0.9326
- Accuracy Es-es: 0.9177
- Accuracy Fr-fr: 0.9444
- Accuracy It-it: 0.9167
- Accuracy Ko-kr: 0.8649
- Accuracy Nl-nl: 0.9450
- Accuracy Pl-pl: 0.9146
- Accuracy Pt-pt: 0.8940
- Accuracy Ru-ru: 0.8667
- Accuracy Zh-cn: 0.7291
- F1: 0.9015
- F1 Cs-cz: 0.9154
- F1 De-de: 0.9467
- F1 En-au: 0.9199
- F1 En-gb: 0.9334
- F1 En-us: 0.9308
- F1 Es-es: 0.9158
- F1 Fr-fr: 0.9436
- F1 It-it: 0.9135
- F1 Ko-kr: 0.8642
- F1 Nl-nl: 0.9440
- F1 Pl-pl: 0.9159
- F1 Pt-pt: 0.8883
- F1 Ru-ru: 0.8646
- F1 Zh-cn: 0.7249
- Loss: 0.4119
- Loss Cs-cz: 0.3790
- Loss De-de: 0.2649
- Loss En-au: 0.3459
- Loss En-gb: 0.2853
- Loss En-us: 0.2203
- Loss Es-es: 0.2731
- Loss Fr-fr: 0.1909
- Loss It-it: 0.3520
- Loss Ko-kr: 0.5431
- Loss Nl-nl: 0.2515
- Loss Pl-pl: 0.4113
- Loss Pt-pt: 0.4798
- Loss Ru-ru: 0.6470
- Loss Zh-cn: 1.1216
- Predict Samples: 4086
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 64
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1500
- num_epochs: 50.0
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Accuracy |
|---|---|---|---|---|---|
| 2.6739 | 5.41 | 200 | 2.5687 | 0.0430 | 0.1190 |
| 1.4953 | 10.81 | 400 | 1.6052 | 0.5550 | 0.5692 |
| 0.6177 | 16.22 | 600 | 0.7927 | 0.8052 | 0.8011 |
| 0.3609 | 21.62 | 800 | 0.5679 | 0.8609 | 0.8609 |
| 0.4972 | 27.03 | 1000 | 0.5944 | 0.8509 | 0.8523 |
| 0.1799 | 32.43 | 1200 | 0.6194 | 0.8623 | 0.8621 |
| 0.1308 | 37.84 | 1400 | 0.5956 | 0.8569 | 0.8548 |
| 0.2298 | 43.24 | 1600 | 0.5201 | 0.8732 | 0.8743 |
| 0.0052 | 48.65 | 1800 | 0.3826 | 0.9106 | 0.9103 |
Framework versions
- Transformers 4.18.0.dev0
- Pytorch 1.10.2+cu113
- Datasets 2.0.1.dev0
- Tokenizers 0.11.6
Model info
Created: 2026-01-21
Updated: 2026-01-21
View on Hugging Face