README.md

metadata

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-large-v3
model-index:
  - name: Whisper Large V3 Thai Combined V1 - biodatlab
    results:
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: mozilla-foundation/common_voice_13_0 th
          type: mozilla-foundation/common_voice_13_0
          config: th
          split: test
          args: th
        metrics:
          - type: wer
            value: 0.21
            name: Wer on Private Insurance Test Set
          - type: wer
            value: 0.41
            name: Wer on Private Contact Center Test Set

Amity Whisper Large Lora V1 (Thai)

This model is a fine-tuned version of biodatlab/whisper-th-large-v3-combined on private enterprise call-center voice datasets. It achieves the following results on the real industry (contact center, insurance) test set:

MODEL	Contact Certer Set ( WER )	Insurance Set ( WER )
biodatlab/whisper-th-large-v3-combined	0.53	0.33
Thai Cloud STT Service	0.47	0.29
amity-whisper-large-stt-th-lora-v1	0.41	0.21
amity-whisper-medium-stt-th-lora-v1	0.41	0.25

Model description

Use the model with huggingface's transformers as follows:

import torch
from transformers import pipeline, AutoModelForSpeechSeq2Seq, AutoProcessor
from peft import PeftModel

BASE_MODEL = "biodatlab/whisper-th-large-v3-combined"  
LORA_MODEL = "amityco/amity-whisper-large-stt-th-lora-v1"  
LANG = "th"

device = 0 if torch.cuda.is_available() else "cpu"

# 1. Load the base Whisper model
base_model = AutoModelForSpeechSeq2Seq.from_pretrained(
    BASE_MODEL,
    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
    low_cpu_mem_usage=True,
    device_map="auto"
)

# 2. Load and merge the LoRA weights
model = PeftModel.from_pretrained(base_model, LORA_MODEL)
model = model.merge_and_unload()

# 3. Load processor/tokenizer
processor = AutoProcessor.from_pretrained(BASE_MODEL)

# 4. Force Thai transcription
model.config.forced_decoder_ids = processor.tokenizer.get_decoder_prompt_ids(
    language=LANG,
    task="transcribe"
)

# 5. Create pipeline
pipe = pipeline(
    task="automatic-speech-recognition",
    model=model,
    tokenizer=processor.tokenizer,
    feature_extractor=processor.feature_extractor,
    chunk_length_s=30,
    device=device,
)

# 6. Run transcription
result = pipe("audio.mp3")["text"]
print(result)

Framework versions

• Transformers 4.37.2
• Pytorch 2.1.0
• Datasets 2.16.1
• Tokenizers 0.15.1