| | --- |
| | language: |
| | - it |
| | license: apache-2.0 |
| | tags: |
| | - whisper-event |
| | - generated_from_trainer |
| | base_model: openai/whisper-medium |
| | datasets: |
| | - mozilla-foundation/common_voice_17_0 |
| | - google/fleurs |
| | - facebook/multilingual_librispeech |
| | - facebook/voxpopuli |
| | metrics: |
| | - wer |
| | model-index: |
| | - name: Whisper Medium Mixed-Italian |
| | results: |
| | - task: |
| | type: automatic-speech-recognition |
| | name: Automatic Speech Recognition |
| | dataset: |
| | name: mozilla-foundation/common_voice_17_0 it |
| | type: mozilla-foundation/common_voice_17_0 |
| | config: it |
| | split: test |
| | args: it |
| | metrics: |
| | - type: wer |
| | value: 6.840122206312234 |
| | name: Wer |
| | - task: |
| | type: automatic-speech-recognition |
| | name: Automatic Speech Recognition |
| | dataset: |
| | name: google/fleurs |
| | type: google/fleurs |
| | config: it_it |
| | split: test |
| | metrics: |
| | - type: wer |
| | value: 3.75 |
| | name: WER |
| | - task: |
| | type: automatic-speech-recognition |
| | name: Automatic Speech Recognition |
| | dataset: |
| | name: facebook/multilingual_librispeech |
| | type: facebook/multilingual_librispeech |
| | config: italian |
| | split: test |
| | metrics: |
| | - type: wer |
| | value: 11.44 |
| | name: WER |
| | - task: |
| | type: automatic-speech-recognition |
| | name: Automatic Speech Recognition |
| | dataset: |
| | name: facebook/voxpopuli |
| | type: facebook/voxpopuli |
| | config: it |
| | split: test |
| | metrics: |
| | - type: wer |
| | value: 17.94 |
| | name: WER |
| | 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 Medium Mixed-Italian |
| |
|
| | This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the it datasets: |
| | - mozilla-foundation/common_voice_17_0 |
| | - google/fleurs |
| | - facebook/multilingual_librispeech |
| | - facebook/voxpopuli |
| |
|
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.1318 |
| | - Wer: 6.8401 |
| |
|
| | ## 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: 32 |
| | - 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: 5000 |
| | - mixed_precision_training: Native AMP |
| | |
| | ### Training results |
| | |
| | | Training Loss | Epoch | Step | Validation Loss | Wer | |
| | |:-------------:|:-----:|:----:|:---------------:|:------:| |
| | | 0.1502 | 0.2 | 1000 | 0.1708 | 9.0922 | |
| | | 0.1584 | 0.4 | 2000 | 0.1554 | 8.1757 | |
| | | 0.1309 | 0.6 | 3000 | 0.1426 | 7.4142 | |
| | | 0.0984 | 0.8 | 4000 | 0.1370 | 7.1298 | |
| | | 0.0933 | 1.0 | 5000 | 0.1318 | 6.8401 | |
| | |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.42.0.dev0 |
| | - Pytorch 2.3.0+cu121 |
| | - Datasets 2.19.1 |
| | - Tokenizers 0.19.1 |
