Monsoon-Whisper-Medium-Gigaspeech2
Monsoon-Whisper-Medium-GigaSpeech2 is a 🇹🇭 Thai Automatic Speech Recognition (ASR) model. It is based on Whisper-Medium and fine-tuned on GigaSpeech2.
Originally developed as a scale experiment for research on emergent capabilities in ASR tasks. It performs well in the wild, including with audio sourced from YouTube and in noisy environments.
More details can be found in our Typhoon-Audio Release Blog.
By using this model, you agree to the OpenTyphoon Terms and Conditions and acknowledge the Privacy Notice: https://opentyphoon.ai/tac · https://opentyphoon.ai/privacy
Model Description
- Model type: Whisper Medium.
- Requirement: transformers 4.38.0 or newer.
- Primary Language(s): Thai 🇹🇭
- License: Apache 2.0
Usage Example
from transformers import WhisperProcessor, WhisperForConditionalGeneration
import torchaudio
import torch
model_path = "scb10x/monsoon-whisper-medium-gigaspeech2"
device = "cuda"
filepath = 'audio.wav'
processor = WhisperProcessor.from_pretrained(model_path)
model = WhisperForConditionalGeneration.from_pretrained(
model_path, torch_dtype=torch.bfloat16
)
model.to(device)
model.eval()
model.config.forced_decoder_ids = processor.get_decoder_prompt_ids(
language="th", task="transcribe"
)
array, sr = torchaudio.load(filepath)
input_features = (
processor(array, sampling_rate=sr, return_tensors="pt")
.to(device)
.to(torch.bfloat16)
.input_features
)
predicted_ids = model.generate(input_features)
transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
print(transcription)
Evaluation Results
| Model | WER (GS2) | WER (CV17) | CER (GS2) | CER (CV17) |
|---|---|---|---|---|
| whisper-large-v3 | 37.02 | 22.63 | 24.03 | 8.49 |
| whisper-medium | 55.64 | 43.01 | 37.55 | 16.41 |
| biodatlab-whisper-th-medium-combined | 31.00 | 14.25 | 21.20 | 5.69 |
| biodatlab-whisper-th-large-v3-combined | 29.02 | 15.72 | 19.96 | 6.32 |
| monsoon-whisper-medium-gigaspeech2 | 22.74 | 20.79 | 14.15 | 6.92 |
Intended Uses & Limitations
This model is experimental and may not always be accurate. Developers should carefully assess potential risks in the context of their specific applications.
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Kunat Pipatanakul, Potsawee Manakul, Sittipong Sripaisarnmongkol, Natapong Nitarach, Warit Sirichotedumrong, Adisai Na-Thalang, Phatrasek Jirabovonvisut, Parinthapat Pengpun, Krisanapong Jirayoot, Pathomporn Chokchainant, Kasima Tharnpipitchai