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---
language:
- th
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_13_0
- google/fleurs
metrics:
- wer
model-index:
- name: Whisper Medium Thai Combined V3 - biodatlab
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: mozilla-foundation/common_voice_11_0 th
type: mozilla-foundation/common_voice_11_0
config: th
split: test
args: th
metrics:
- name: Wer
type: wer
value: 8.44
library_name: transformers
---
<!-- 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 (Thai): Combined V3
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on augmented versions of the mozilla-foundation/common_voice_13_0 th, google/fleurs, and curated datasets.
It achieves the following results (NOT-UP-TO-DATE) on the common-voice-11 evaluation set:
- Loss: 0.1475
- WER: 13.03 (without Tokenizer)
- WER: 8.44 (with Deepcut Tokenizer)
## Model description
Use the model with huggingface's `transformers` as follows:
```py
from transformers import pipeline
MODEL_NAME = "biodatlab/whisper-th-medium-combined" # specify the model name
lang = "th" # change to Thai langauge
device = 0 if torch.cuda.is_available() else "cpu"
pipe = pipeline(
task="automatic-speech-recognition",
model=MODEL_NAME,
chunk_length_s=30,
device=device,
)
pipe.model.config.forced_decoder_ids = pipe.tokenizer.get_decoder_prompt_ids(
language=lang,
task="transcribe"
)
text = pipe("audio.mp3")["text"] # give audio mp3 and transcribe text
```
## 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: 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0679 | 2.09 | 5000 | 0.1475 | 13.03 |
### Framework versions
- Transformers 4.31.0.dev0
- Pytorch 2.1.0
- Datasets 2.13.1
- Tokenizers 0.13.3
## Citation
Cite using Bibtex:
```
@misc {thonburian_whisper_med,
author = { Atirut Boribalburephan, Zaw Htet Aung, Knot Pipatsrisawat, Titipat Achakulvisut },
title = { Thonburian Whisper: A fine-tuned Whisper model for Thai automatic speech recognition },
year = 2022,
url = { https://huggingface.co/biodatlab/whisper-th-medium-combined },
doi = { 10.57967/hf/0226 },
publisher = { Hugging Face }
}
``` |