File size: 3,125 Bytes
d846ef3 330b786 d846ef3 fe4293a d846ef3 330b786 d846ef3 f33bc23 d846ef3 330b786 d846ef3 330b786 c98d215 330b786 d846ef3 f33bc23 d846ef3 dd45659 fe4293a d846ef3 c98d215 d846ef3 fe4293a d846ef3 fe4293a ccf2594 c1a558c ccf2594 e3d1473 ccf2594 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 |
---
language:
- th
license: apache-2.0
library_name: transformers
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_13_0
- google/fleurs
metrics:
- wer
base_model: openai/whisper-medium
model-index:
- name: Whisper Medium Thai Combined V3 - biodatlab
results:
- task:
type: automatic-speech-recognition
name: 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:
- type: wer
value: 8.44
name: Wer
---
<!-- 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-medium-th-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 }
}
``` |