|
--- |
|
language: |
|
- ro |
|
license: apache-2.0 |
|
tags: |
|
- whisper-event |
|
pinned: true |
|
datasets: |
|
- mozilla-foundation/common_voice_11_0 |
|
- gigant/romanian_speech_synthesis_0_8_1 |
|
model-index: |
|
- name: Whisper Medium Romanian |
|
results: |
|
- task: |
|
name: Automatic Speech Recognition |
|
type: automatic-speech-recognition |
|
dataset: |
|
name: mozilla-foundation/common_voice_11_0 ro |
|
type: mozilla-foundation/common_voice_11_0 |
|
config: ro |
|
split: test |
|
args: ro |
|
metrics: |
|
- name: Wer |
|
type: wer |
|
value: 4.73 |
|
- task: |
|
name: Automatic Speech Recognition |
|
type: automatic-speech-recognition |
|
dataset: |
|
name: google/fleurs ro |
|
type: google/fleurs |
|
config: ro |
|
split: test |
|
args: ro |
|
metrics: |
|
- name: Wer |
|
type: wer |
|
value: 19.64 |
|
metrics: |
|
- wer |
|
--- |
|
|
|
# Whisper Medium Romanian |
|
|
|
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. |
|
It achieves the following results on the evaluation set: |
|
- eval_loss: 0.06453 |
|
- eval_wer: 4.717 |
|
- epoch: 7.03 |
|
- step: 3500 |
|
|
|
## Model description |
|
|
|
The architecture is the same as [openai/whisper-medium](https://huggingface.co/openai/whisper-medium). |
|
|
|
## Training and evaluation data |
|
|
|
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. |
|
|
|
## Usage |
|
Inference with 🤗 transformers |
|
```python |
|
from transformers import WhisperProcessor, WhisperForConditionalGeneration |
|
from datasets import Audio, load_dataset |
|
import torch |
|
|
|
# load model and processor |
|
processor = WhisperProcessor.from_pretrained("gigant/whisper-medium-romanian") |
|
model = WhisperForConditionalGeneration.from_pretrained("gigant/whisper-medium-romanian") |
|
|
|
# load dummy dataset and read soundfiles |
|
ds = load_dataset("common_voice", "ro", split="test", streaming=True) |
|
ds = ds.cast_column("audio", Audio(sampling_rate=16_000)) |
|
input_speech = next(iter(ds))["audio"]["array"] |
|
model.config.forced_decoder_ids = processor.get_decoder_prompt_ids(language = "ro", task = "transcribe") |
|
input_features = processor(input_speech, return_tensors="pt", sampling_rate=16_000).input_features |
|
predicted_ids = model.generate(input_features, max_length=448) |
|
# transcription = processor.batch_decode(predicted_ids) |
|
transcription = processor.batch_decode(predicted_ids, skip_special_tokens = True) |
|
``` |
|
|
|
The code was adapted from [openai/whisper-medium](https://huggingface.co/openai/whisper-medium). |
|
|
|
## Training procedure |
|
|
|
### Training hyperparameters |
|
|
|
The following hyperparameters were used during training: |
|
- learning_rate: 1e-05 |
|
- train_batch_size: 32 |
|
- eval_batch_size: 32 |
|
- 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 |
|
|
|
### Framework versions |
|
|
|
- Transformers 4.26.0.dev0 |
|
- Pytorch 1.13.0+cu117 |
|
- Datasets 2.7.1.dev0 |
|
- Tokenizers 0.13.2 |
