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--- |
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language: |
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- mn |
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base_model: openai/whisper-medium |
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library_name: transformers |
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datasets: |
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- mozilla-foundation/common_voice_17_0 |
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- google/fleurs |
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tags: |
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- audio |
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- automatic-speech-recognition |
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widget: |
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- example_title: Common Voice sample 1 |
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src: sample1.flac |
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- example_title: Common Voice sample 2 |
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src: sample2.flac |
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model-index: |
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- name: whisper-medium-mn |
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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: Common Voice 17.0 |
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type: common_voice_17_0 |
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config: mn |
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split: test |
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args: |
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language: mn |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 12.9580 |
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pipeline_tag: automatic-speech-recognition |
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license: apache-2.0 |
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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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# Whisper Medium Mn - Erkhembayar Gantulga |
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This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Common Voice 17.0 and Google Fleurs datasets. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1083 |
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- Wer: 12.9580 |
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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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Datasets used for training: |
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- [Common Voice 17.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_17_0) |
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- [Google Fleurs](https://huggingface.co/datasets/google/fleurs) |
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For training, combined Common Voice 17.0 and Google Fleurs datasets: |
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``` |
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from datasets import load_dataset, DatasetDict, concatenate_datasets |
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from datasets import Audio |
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common_voice = DatasetDict() |
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common_voice["train"] = load_dataset("mozilla-foundation/common_voice_17_0", "mn", split="train+validation+validated", use_auth_token=True) |
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common_voice["test"] = load_dataset("mozilla-foundation/common_voice_17_0", "mn", split="test", use_auth_token=True) |
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common_voice = common_voice.cast_column("audio", Audio(sampling_rate=16000)) |
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common_voice = common_voice.remove_columns( |
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["accent", "age", "client_id", "down_votes", "gender", "locale", "path", "segment", "up_votes", "variant"] |
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) |
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google_fleurs = DatasetDict() |
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google_fleurs["train"] = load_dataset("google/fleurs", "mn_mn", split="train+validation", use_auth_token=True) |
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google_fleurs["test"] = load_dataset("google/fleurs", "mn_mn", split="test", use_auth_token=True) |
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google_fleurs = google_fleurs.remove_columns( |
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["id", "num_samples", "path", "raw_transcription", "gender", "lang_id", "language", "lang_group_id"] |
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) |
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google_fleurs = google_fleurs.rename_column("transcription", "sentence") |
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dataset = DatasetDict() |
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dataset["train"] = concatenate_datasets([common_voice["train"], google_fleurs["train"]]) |
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dataset["test"] = concatenate_datasets([common_voice["test"], google_fleurs["test"]]) |
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``` |
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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: 1e-05 |
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- train_batch_size: 16 |
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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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- training_steps: 4000 |
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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 | |
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|:-------------:|:------:|:----:|:---------------:|:-------:| |
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| 0.2986 | 0.4912 | 500 | 0.3557 | 40.1515 | |
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| 0.2012 | 0.9823 | 1000 | 0.2310 | 28.3512 | |
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| 0.099 | 1.4735 | 1500 | 0.1864 | 23.4453 | |
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| 0.0733 | 1.9646 | 2000 | 0.1405 | 18.3024 | |
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| 0.0231 | 2.4558 | 2500 | 0.1308 | 16.5645 | |
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| 0.0191 | 2.9470 | 3000 | 0.1155 | 14.5569 | |
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| 0.0059 | 3.4381 | 3500 | 0.1122 | 13.4728 | |
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| 0.006 | 3.9293 | 4000 | 0.1083 | 12.9580 | |
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### Framework versions |
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- Transformers 4.44.0 |
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- Pytorch 2.3.1+cu121 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
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