safetensors whisper ms en zh ta dataset:mesolitica/Malaysian-STT-Whisper dataset:malaysia-ai/STT-Whisper base_model:openai/whisper-small base_model:finetune:openai/whisper-small region:us
---
base_model:
- openai/whisper-small
datasets:
- mesolitica/Malaysian-STT-Whisper
- malaysia-ai/STT-Whisper
language:
- ms
- en
- zh
- ta
---

# Malaysian Finetune Whisper Small

Finetune Whisper Small on Malaysian context.

## Improvement

1. Distilled from Whisper Large V3 on Malaysian and Science context.
2. Better translation for Malay, Manglish, Mandarin, Tamil and Science context.
3. Word level timestamp, introduced `<|transcribeprecise|>` token, **a new task!**

## how we finetuned it?

We done 2 phases,

1. Finetune on [mesolitica/Malaysian-STT-Whisper](https://huggingface.co/datasets/mesolitica/Malaysian-STT-Whisper)
- Revision [32be8a01365c012877ea2f91a2b160c8f1ddbddd](https://huggingface.co/malaysia-ai/malaysian-whisper-small/commit/32be8a01365c012877ea2f91a2b160c8f1ddbddd)
- WanDB at https://wandb.ai/huseinzol05/malaysian-whisper-small-v2
2. Annealing on 5% from [mesolitica/Malaysian-STT-Whisper](https://huggingface.co/datasets/mesolitica/Malaysian-STT-Whisper) and 100% from [malaysia-ai/STT-Whisper](https://huggingface.co/datasets/malaysia-ai/STT-Whisper), **still on training**

## how to

Load the model,

```python
import torch
from transformers.models.whisper import tokenization_whisper

tokenization_whisper.TASK_IDS = ["translate", "transcribe", "transcribeprecise"]

from transformers import WhisperForConditionalGeneration, WhisperProcessor

processor = WhisperProcessor.from_pretrained(
    'malaysia-ai/malaysian-whisper-small'
)
tokenizer = processor.tokenizer
model = WhisperForConditionalGeneration.from_pretrained(
    'malaysia-ai/malaysian-whisper-small', torch_dtype = torch.bfloat16
).cuda().eval()
```

### Transcribe

```python
from datasets import Audio
import requests

sr = 16000
audio = Audio(sampling_rate=sr)

r = requests.get('https://github.com/mesolitica/malaya-speech/raw/master/speech/assembly.mp3')
y = audio.decode_example(audio.encode_example(r.content))['array']

with torch.no_grad():
    p = processor([y], return_tensors='pt')
    p['input_features'] = p['input_features'].to(torch.bfloat16)
    r = model.generate(
        p['input_features'].cuda(),
        output_scores=True,
        return_dict_in_generate=True,
        language='ms',
        return_timestamps=True, task = 'transcribe')

tokenizer.convert_tokens_to_string(tokenizer.convert_ids_to_tokens(r.sequences[0]))
```

```
<|startoftranscript|><|ms|><|transcribe|><|0.02|> Assembly on Aging di Vienna, Australia<|3.78|><|3.78|> yang telah diadakan pada tahun 1982<|6.50|><|6.50|> dan berasaskan unjuran tersebut<|8.82|><|8.82|> maka Jabatan Perangkaan Malaysia<|10.40|><|10.40|> menganggarkan menjelang tahun 2035<|13.72|><|13.72|> sejumlah 15% penduduk kita adalah daripada kalangan warga emas.<|18.72|><|19.28|> Untuk makluman Tuan Yang Pertua dan juga Alia Mbahumat,<|22.12|><|22.26|> pembangunan sistem pendaftaran warga emas<|24.02|><|24.02|> ataupun kita sebutkan event<|25.38|><|25.38|> adalah usaha kerajaan ke arah merealisasikan<|28.40|><|endoftext|>
```

### Transcribe word level timestamp

You must use `transcribeprecise` for the task, or `<|transcribeprecise|>` token,

```python
from datasets import Audio
import requests

sr = 16000
audio = Audio(sampling_rate=sr)

r = requests.get('https://github.com/mesolitica/malaya-speech/raw/master/speech/assembly.mp3')
y = audio.decode_example(audio.encode_example(r.content))['array']

with torch.no_grad():
    p = processor([y], return_tensors='pt')
    p['input_features'] = p['input_features'].to(torch.bfloat16)
    r = model.generate(
        p['input_features'].cuda(),
        output_scores=True,
        return_dict_in_generate=True,
        language='ms',
        return_timestamps=True, task = 'transcribeprecise')

tokenizer.convert_tokens_to_string(tokenizer.convert_ids_to_tokens(r.sequences[0]))
```

```
<|startoftranscript|><|ms|><|transcribeprecise|><|0.02|> Assembly<|1.20|><|1.56|> on<|1.64|><|1.74|> Aging<|2.04|><|2.14|> di<|2.22|><|2.26|> Vienna<|2.50|><|2.72|> Australia<|3.12|><|4.26|> yang<|4.38|><|4.42|> telah<|4.58|><|4.62|> diadakan<|5.08|><|5.16|> pada<|5.30|><|5.36|> tahun<|5.60|><|5.62|> 1982<|6.92|><|7.12|> dan<|7.24|><|7.32|> berasaskan<|7.88|><|7.98|> unjuran<|8.36|><|8.42|> tersebut<|8.80|><|8.88|> maka<|9.06|><|9.12|> Jabatan<|9.48|><|9.56|> Perangkaan<|9.98|><|10.04|> Malaysia<|10.36|><|10.84|> menganggarkan<|11.56|><|11.98|> menjelang<|12.34|><|12.40|> tahun<|12.64|><|12.66|> 2035<|14.08|><|14.50|> sejumlah<|14.96|><|14.98|> 15%<|16.14|><|16.26|> penduduk<|16.62|><|16.68|> kita<|16.90|><|17.02|> adalah<|17.30|><|17.40|> daripada<|17.80|><|17.86|> kalangan<|18.16|><|18.22|> warga<|18.40|><|18.46|> emas.<|18.68|><|19.24|> Untuk<|19.40|><|19.46|> makluman<|19.86|><|20.64|> Tuan<|20.76|><|20.82|> Yang<|20.90|><|20.94|> Pertua<|21.14|><|21.20|> dan<|21.28|><|21.34|> juga<|21.50|><|21.58|> Alia<|21.70|><|21.76|> Mbah<|21.88|><|21.92|> Ahmad,<|22.08|><|22.22|> pembangunan<|22.66|><|22.72|> sistem<|23.00|><|23.06|> pendaftaran<|23.48|><|23.54|> warga<|23.72|><|23.78|> emas<|23.98|><|24.06|> ataupun<|24.36|><|24.42|> kita<|24.56|><|24.62|> sebutkan<|24.94|><|25.08|> event<|25.38|><|25.86|> adalah<|26.10|><|26.18|> usaha<|26.46|><|26.60|> kerajaan<|27.06|><|27.16|> kearah<|27.44|><|27.50|> merealisasikan<|28.36|><|28.86|> objektif<|29.36|><|29.42|> yang<|29.52|><|29.56|> telah<|29.72|><|29.76|> digarakan<|30.00|><|endoftext|>
```

**Make sure you already monkey patched `tokenization_whisper.TASK_IDS = ["translate", "transcribe", "transcribeprecise"]` at starting of your script**.