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windanam_mms-1b-tts_v2

Automatic Speech Recognition
Transformers
Safetensors
Fula
wav2vec2
Generated from Trainer
Inference Endpoints
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metadata
license: cc-by-nc-4.0
base_model: facebook/mms-1b-all
tags:
  - generated_from_trainer
metrics:
  - wer
model-index:
  - name: windanam_mms-1b-tts_v2
    results: []
datasets:
  - cawoylel/FulaSpeechCorpora-splited-noise_augmented
  - cawoylel/FulaNewsTextCorporaTTS
language:
  - ff
pipeline_tag: automatic-speech-recognition

windanam_mms-1b-tts_v2

This model is a fine-tuned version of facebook/mms-1b-all on FulaSpeechCorpora-splited-noi_augmented and FulaNewsTextCorporaTTS dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4020
  • Wer: 0.2416

If you want to test quickly the model, instead of using the huggingface Inference API, use this space: https://huggingface.co./spaces/cawoylel/MMS-ASR-Fula

How to use

Load Model

pipe = pipeline("automatic-speech-recognition", model="cawoylel/windanam_mms-1b-tts_v2")

Run Model

transcription = pipe(your_audio_file)

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 1e-05
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 500
  • training_steps: 100000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
0.8993 0.06 2500 0.6557 0.3266
0.8056 0.12 5000 0.5806 0.3130
0.7663 0.17 7500 0.5514 0.2804
0.736 0.23 10000 0.5333 0.2729
0.7111 0.29 12500 0.5200 0.2686
0.6867 0.35 15000 0.5043 0.2657
0.6942 0.41 17500 0.4933 0.2617
0.6695 0.47 20000 0.4814 0.2595
0.6464 0.52 22500 0.4733 0.2576
0.694 0.58 25000 0.4618 0.2553
0.6223 0.64 27500 0.4574 0.2544
0.6692 0.7 30000 0.4495 0.2526
0.6337 0.76 32500 0.4454 0.2518
0.6055 0.81 35000 0.4396 0.2501
0.6436 0.87 37500 0.4341 0.2498
0.6389 0.93 40000 0.4304 0.2488
0.6308 0.99 42500 0.4260 0.2486
0.6167 1.05 45000 0.4248 0.2469
0.6253 1.11 47500 0.4201 0.2467
0.603 1.16 50000 0.4179 0.2457
0.5864 1.22 52500 0.4187 0.2449
0.6276 1.28 55000 0.4147 0.2446
0.5901 1.34 57500 0.4151 0.2439
0.5877 1.4 60000 0.4126 0.2436
0.604 1.45 62500 0.4111 0.2435
0.6182 1.51 65000 0.4097 0.2436
0.5745 1.57 67500 0.4074 0.2433
0.5843 1.63 70000 0.4076 0.2429
0.578 1.69 72500 0.4062 0.2429
0.5799 1.75 75000 0.4060 0.2425
0.6017 1.8 77500 0.4047 0.2423
0.5688 1.86 80000 0.4052 0.2417
0.5837 1.92 82500 0.4039 0.2418
0.5801 1.98 85000 0.4038 0.2414
0.5965 2.04 87500 0.4030 0.2413
0.5827 2.09 90000 0.4022 0.2414
0.6043 2.15 92500 0.4020 0.2414
0.609 2.21 95000 0.4021 0.2414
0.5827 2.27 97500 0.4021 0.2415
0.5755 2.33 100000 0.4020 0.2416

Framework versions

  • Transformers 4.36.0.dev0
  • Pytorch 2.1.1+cu121
  • Datasets 2.15.0
  • Tokenizers 0.15.0