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README.md
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### Training Data
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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[More Information Needed]
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### Training Procedure
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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#### Preprocessing [optional]
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[More Information Needed]
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#### Training Hyperparameters
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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#### Speeds, Sizes, Times [optional]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
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## Evaluation
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<!-- This section describes the evaluation protocols and provides the results. -->
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### Testing Data, Factors & Metrics
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#### Testing Data
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<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
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#### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
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#### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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[More Information Needed]
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### Results
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[More Information Needed]
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#### Summary
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## Model Examination [optional]
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<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed]
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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## Technical Specifications [optional]
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### Model Architecture and Objective
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[More Information Needed]
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### Compute Infrastructure
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[More Information Needed]
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#### Hardware
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[More Information Needed]
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#### Software
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[More Information Needed]
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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[More Information Needed]
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**APA:**
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[More Information Needed]
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## Glossary [optional]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
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## More Information [optional]
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[More Information Needed]
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## Model Card Authors [optional]
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## Model Card Contact
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[More Information Needed]
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---
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license: cc-by-nc-4.0
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base_model: facebook/mms-1b-all
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tags:
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- generated_from_trainer
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datasets:
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- audiofolder
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metrics:
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- wer
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model-index:
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- name: wav2vec2-large-mms-1b-even-pakendorf
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results:
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- task:
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name: Automatic Speech Recognition
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type: automatic-speech-recognition
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dataset:
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name: audiofolder
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type: audiofolder
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config: default
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split: train
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args: default
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metrics:
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- name: Wer
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type: wer
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value: 0.7591335595927331
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# wav2vec2-large-mms-1b-even-pakendorf
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This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on the audiofolder dataset.
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It achieves the following results on the evaluation set:
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- Loss: inf
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- Wer: 0.7591
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- Cer: 0.2779
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 0.001
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- train_batch_size: 4
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- eval_batch_size: 8
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_steps: 100
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- num_epochs: 4
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- mixed_precision_training: Native AMP
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
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|:-------------:|:------:|:----:|:---------------:|:------:|:------:|
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| 1.847 | 0.1895 | 300 | inf | 0.9027 | 0.3662 |
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| 1.8253 | 0.3790 | 600 | inf | 0.9087 | 0.3658 |
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| 1.6956 | 0.5685 | 900 | inf | 0.8723 | 0.3412 |
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| 1.6616 | 0.7581 | 1200 | inf | 0.8437 | 0.3209 |
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| 1.5962 | 0.9476 | 1500 | inf | 0.8392 | 0.3217 |
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| 1.6299 | 1.1371 | 1800 | inf | 0.8447 | 0.3201 |
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| 1.5242 | 1.3266 | 2100 | inf | 0.8191 | 0.3076 |
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| 1.582 | 1.5161 | 2400 | inf | 0.8157 | 0.3070 |
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| 1.5555 | 1.7056 | 2700 | inf | 0.8092 | 0.3061 |
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| 1.5476 | 1.8951 | 3000 | inf | 0.7999 | 0.3009 |
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| 1.4725 | 2.0846 | 3300 | inf | 0.7945 | 0.2952 |
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| 1.4902 | 2.2742 | 3600 | inf | 0.7834 | 0.2936 |
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| 1.3984 | 2.4637 | 3900 | inf | 0.7836 | 0.2900 |
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| 1.4633 | 2.6532 | 4200 | inf | 0.7942 | 0.2872 |
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| 1.4533 | 2.8427 | 4500 | inf | 0.7804 | 0.2863 |
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| 1.4814 | 3.0322 | 4800 | inf | 0.7728 | 0.2859 |
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| 1.4397 | 3.2217 | 5100 | inf | 0.7693 | 0.2818 |
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| 1.4218 | 3.4112 | 5400 | inf | 0.7702 | 0.2831 |
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| 1.3655 | 3.6008 | 5700 | inf | 0.7650 | 0.2795 |
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| 1.34 | 3.7903 | 6000 | inf | 0.7615 | 0.2792 |
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| 1.3351 | 3.9798 | 6300 | inf | 0.7591 | 0.2779 |
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### Framework versions
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- Transformers 4.42.0.dev0
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- Pytorch 2.3.1+cu121
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- Datasets 2.19.2
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- Tokenizers 0.19.1
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adapter.eve.safetensors
ADDED
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version https://git-lfs.github.com/spec/v1
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oid sha256:4efa3d821f2b60b81c838e88baae15d00258eaacf5039207b0125010f91aee92
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size 8844656
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