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--- |
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language: |
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- ro |
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license: apache-2.0 |
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tags: |
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- whisper-event |
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datasets: |
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- mozilla-foundation/common_voice_11_0 |
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- gigant/romanian_speech_synthesis_0_8_1 |
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metrics: |
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- wer |
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pinned: true |
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base_model: openai/whisper-medium |
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model-index: |
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- name: Whisper Medium Romanian |
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results: |
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- task: |
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type: automatic-speech-recognition |
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name: Automatic Speech Recognition |
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dataset: |
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name: mozilla-foundation/common_voice_11_0 ro |
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type: mozilla-foundation/common_voice_11_0 |
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config: ro |
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split: test |
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args: ro |
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metrics: |
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- type: wer |
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value: 4.73 |
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name: Wer |
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- task: |
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type: automatic-speech-recognition |
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name: Automatic Speech Recognition |
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dataset: |
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name: google/fleurs ro |
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type: google/fleurs |
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config: ro |
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split: test |
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args: ro |
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metrics: |
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- type: wer |
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value: 19.64 |
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name: Wer |
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--- |
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# Whisper Medium Romanian |
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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 11.0 dataset, and the Romanian speech synthesis corpus. |
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It achieves the following results on the evaluation set: |
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- eval_loss: 0.06453 |
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- eval_wer: 4.717 |
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- epoch: 7.03 |
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- step: 3500 |
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## Model description |
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The architecture is the same as [openai/whisper-medium](https://huggingface.co/openai/whisper-medium). |
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## Training and evaluation data |
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The model was trained on the Common Voice 11.0 dataset (`train+validation+other` splits) and the Romanian speech synthesis corpus, and was tested on the `test` split of the Common Voice 11.0 dataset. |
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## Usage |
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Inference with 🤗 transformers |
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```python |
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from transformers import WhisperProcessor, WhisperForConditionalGeneration |
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from datasets import Audio, load_dataset |
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import torch |
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# load model and processor |
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processor = WhisperProcessor.from_pretrained("gigant/whisper-medium-romanian") |
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model = WhisperForConditionalGeneration.from_pretrained("gigant/whisper-medium-romanian") |
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# load dummy dataset and read soundfiles |
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ds = load_dataset("common_voice", "ro", split="test", streaming=True) |
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ds = ds.cast_column("audio", Audio(sampling_rate=16_000)) |
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input_speech = next(iter(ds))["audio"]["array"] |
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model.config.forced_decoder_ids = processor.get_decoder_prompt_ids(language = "ro", task = "transcribe") |
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input_features = processor(input_speech, return_tensors="pt", sampling_rate=16_000).input_features |
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predicted_ids = model.generate(input_features, max_length=448) |
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# transcription = processor.batch_decode(predicted_ids) |
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transcription = processor.batch_decode(predicted_ids, skip_special_tokens = True) |
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``` |
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The code was adapted from [openai/whisper-medium](https://huggingface.co/openai/whisper-medium). |
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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: 32 |
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- eval_batch_size: 32 |
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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: 500 |
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- training_steps: 5000 |
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- mixed_precision_training: Native AMP |
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### Framework versions |
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- Transformers 4.26.0.dev0 |
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- Pytorch 1.13.0+cu117 |
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- Datasets 2.7.1.dev0 |
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- Tokenizers 0.13.2 |
