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Whisper Tiny Es Mix Norm

This model is a fine-tuned version of openai/whisper-tiny on the following datasets: deepdml/commonvoice260 disco-eth/WorldSpeech facebook/voxpopuli facebook/multilinguallibrispeech deepdml/voxforge google/fleurs...

Fuentehuggingface.co/deepdml/whisper-tiny-es-mix-norm/tree/c967c037e8bfa9f7b4db9119dae5faf0c0ed86eb
TipoText modelModelo baseopenai/whisper-tinyVersiónmodelDcalidad

Fuente del modelo

Tipo
Text model
Tamaño del archivo
144 MB
Modelo base
openai/whisper-tiny
Versión
vmodel
Actualizado
28 oct 2026
Descargas
194
Fuente
Hugging Face

Descripción de la fuente

Whisper Tiny es

This model is a fine-tuned version of openai/whisper-tiny on the following datasets:

  • deepdml/common_voice_26_0
  • disco-eth/WorldSpeech
  • facebook/voxpopuli
  • facebook/multilingual_librispeech
  • deepdml/voxforge
  • google/fleurs
  • deepdml/basque_parliament_1

It achieves the following results on the evaluation set:

  • Best checkpoint: 44,000
  • Loss: 0.2888
  • Wer: 16.0693
  • Cer: 6.0413

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: 1e-05
  • train_batch_size: 128
  • eval_batch_size: 128
  • seed: 42
  • optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 0.04
  • training_steps: 46000

Training results

Training LossEpochStepValidation LossWer RawCer RawWerCer
0.36630.021710000.597131.872212.147831.651012.1036
0.26250.043520000.514028.278910.556728.234510.5484
0.21830.065230000.476325.38939.274525.36839.2709
0.23200.087040000.443524.30339.007324.29449.0057
0.32900.108750000.436323.96739.111723.95909.1103
0.20300.130460000.411822.94288.574122.93908.5735
0.34560.152270000.413122.71908.658322.71398.6574
0.32840.173980000.399622.67528.456722.67278.4563
0.23360.195790000.369220.92367.928820.92237.9286
0.29500.2174100000.362221.14368.278821.14368.2788
0.38750.2391110000.357520.11977.546020.11977.5460
0.18841.0020120000.340619.39067.230819.39067.2308
0.13701.0237130000.337319.32157.362519.32157.3625
0.14261.0455140000.336419.12377.190319.12377.1903
0.16161.0672150000.332918.57856.959218.57856.9592
0.15601.0889160000.327819.09527.397719.09527.3977
0.15111.1107170000.330818.78777.280318.78777.2803
0.16421.1324180000.327618.35736.782318.35736.7823
0.24951.1542190000.330018.61087.057918.61087.0579
0.19031.1759200000.323218.45047.056118.45047.0561
0.19821.1976210000.316118.36367.071018.36367.0710
0.38911.2194220000.315418.52407.178718.52407.1787
0.27251.2411230000.315517.82606.692017.82606.6920
0.12712.0040240000.301617.15406.564617.15406.5646
0.11712.0257250000.309017.27006.555017.27006.5550
0.17382.0474260000.303717.37216.601517.37216.6015
0.15322.0692270000.306917.19776.433017.19776.4330
0.22202.0909280000.305617.41396.597917.41396.5979
0.15092.1127290000.301016.99236.529716.99236.5297
0.14842.1344300000.303316.78636.208416.78636.2084
0.15582.1561310000.302117.14076.501617.14076.5016
0.15632.1779320000.297916.66776.223616.66776.2236
0.28232.1996330000.300617.18506.491217.18506.4912
0.14632.2213340000.295516.60186.306816.60186.3068
0.12092.2431350000.291616.63736.395016.63736.3950
0.11453.0059360000.294116.88016.504816.88016.5048
0.15453.0277370000.292516.62086.409516.62086.4095
0.32113.0494380000.290516.63546.496616.63546.4966
0.19143.0712390000.292616.26456.148716.26456.1487
0.16723.0929400000.293616.55176.282816.55176.2828
0.12563.1146410000.291016.68616.522016.68616.5220
0.10233.1364420000.291816.41796.237616.41796.2376
0.15973.1581430000.291316.61516.425716.61516.4257
0.19123.1798440000.288816.06936.041316.06936.0413
0.14593.2016450000.289116.23796.110716.23796.1107
0.24853.2233460000.288916.34506.185216.34506.1852

Framework versions

  • Transformers 5.14.1
  • Pytorch 2.6.0+cu124
  • Datasets 5.0.1
  • Tokenizers 0.22.2

Citation

Please cite the model using the following BibTeX entry:

@misc{deepdml/whisper-tiny-es-mix-norm,
      title={Fine-tuned Whisper tiny ASR model for speech recognition in Spanish},
      author={Jimenez, David},
      howpublished={\url{https://huggingface.co/deepdml/whisper-tiny-es-mix-norm}},
      year={2026}
    }

Fuentes

1 fuente
Hugging FacePrincipalVersión c967c037e8bfa9f7b4db9119dae5faf0c0ed86eb
huggingface.co/deepdml/whisper-tiny-es-mix-norm/tree/c967c037e8bfa9f7b4db9119dae5faf0c0ed86eb

Verificado 28 oct

Artefactos del modelo

1 artefacto

model.safetensors

safetensors · 144 MB · SHA-256 95d6c5b5026c…9399 · Hugging Face

Download file

Extractos de fuentes

3 extractos
Readme28 octhuggingface.co/deepdml/whisper-tiny-es-mix-norm

--- library_name: transformers language: - es license: apache-2.0 base_model: openai/whisper-tiny tags: - asr datasets: - deepdml/common_voice_26_0 - disco-eth/WorldSpeech - facebook/voxpopuli - facebook/multilingual_librispeech - deepdml/voxforge - google/fleurs - deepdml/basque_parliament_1 metrics: - wer model-index: - name: Whisper Tiny es results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 26.0 type: deepdml/common_voice_26_0 metrics: - name: Wer type: wer value: 16.345032205710808 pipeline_tag: automatic-speech-recognition --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Tiny es This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the following datasets: - deepdml/common_voice_26_0 - disco-eth/WorldSpeech - facebook/voxpopuli - facebook/multilingual_librispeech - deepdml/voxforge - google/fleurs - deepdml/basque_parliament_1 It achieves the following results on the evaluation set: - Best checkpoint: 44,000 - Loss: 0.2888 - Wer: 16.0693 - Cer: 6.0413 ## 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: 1e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 0.04 - training_steps: 46000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer Raw | Cer Raw | Wer | Cer | |:-------------:|:------:|:-----:|:---------------:|:-------:|:-------:|:-------:|:-------:| | 0.3663 | 0.0217 | 1000 | 0.5971 | 31.8722 | 12.1478 | 31.6510 | 12.1036 | | 0.2625 | 0.0435 | 2000 | 0.5140 | 28.2789 | 10.5567 | 28.2345 | 10.5484 | | 0.2183 | 0.0652 | 3000 | 0.4763 | 25.3893 | 9.2745 | 25.3683 | 9.2709 | | 0.2320 | 0.0870 | 4000 | 0.4435 | 24.3033 | 9.0073 | 24.2944 | 9.0057 | | 0.3290 | 0.1087 | 5000 | 0.4363 | 23.9673 | 9.1117 | 23.9590 | 9.1103 | | 0.2030...

Readme9 agohuggingface.co/deepdml/whisper-tiny-es-mix-norm

--- library_name: transformers language: - es license: apache-2.0 base_model: openai/whisper-tiny tags: - generated_from_trainer datasets: - deepdml/voxforge - fixie-ai/common_voice_17_0 - google/fleurs - deepdml/basque_parliament_1 - facebook/multilingual_librispeech - facebook/voxpopuli metrics: - wer model-index: - name: Whisper Tiny es results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 17.0 type: deepdml/voxforge metrics: - name: Wer type: wer value: 21.674748489027575 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Tiny es This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the Common Voice 17.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.3749 - Wer Raw: 21.6741 - Cer Raw: 8.4585 - Wer: 21.6747 - Cer: 8.4585 ## 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: 1e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.04 - training_steps: 20000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer Raw | Cer Raw | Wer | Cer | |:-------------:|:------:|:-----:|:---------------:|:-------:|:-------:|:-------:|:-------:| | 0.3891 | 0.05 | 1000 | 0.6939 | 37.6291 | 14.8103 | 37.4810 | 14.7830 | | 0.6597 | 0.1 | 2000 | 0.6883 | 34.8302 | 14.4460 | 34.8016 | 14.4405 | | 0.1388 | 0.15 | 3000 | 0.6530 | 34.3358 | 13.1348 | 34.2976 | 13.1269 | | 0.1217 | 0.2 | 4000 | 0.6682 | 34.5982 | 13.2295 | 34.5646 | 13.2233 | | 0.1101 | 0.25 | 5000 | 0.6721 | 35.2325 | 13.7696 | 35.1969 | 13.7628 | | 0.3564 | 0.3 | 6000 | 0.5374 | 28.6885 | 11.2075 | 28.6529 | 11.2010 | | 0.3803 | 0.35 | 7000 | 0.3890 | 20.9394 | 7.6348 | 20.9369 | 7.6342 | | 0.3373 | 0.4 | 8000 | 0.3540 | 19.9264 | 7.3914 | 19.9258 | 7.3913 | | 0.2828 |...

Note28 octhuggingface.co/deepdml/whisper-tiny-es-mix-norm

Source context: 194 downloads · 2 likes · Pipeline automatic-speech-recognition · Library transformers · Repo deepdml/whisper-tiny-es-mix-norm