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---
base_model:
- openai/whisper-large-v3-turbo
datasets:
- mozilla-foundation/common_voice_17_0
- google/fleurs
language:
- th
library_name: transformers
pipeline_tag: automatic-speech-recognition
---
```python
from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
import torch
# MODEL_NAME = "whisper-large-v3-turbo-thai"
MODEL_NAME = "whis"
device = "cuda:0" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
model = AutoModelForSpeechSeq2Seq.from_pretrained(
MODEL_NAME,
torch_dtype=torch_dtype,
# low_cpu_mem_usage=True,
# use_safetensors=True,
)
model.to(device)
processor = AutoProcessor.from_pretrained(MODEL_NAME)
whisper = pipeline(
"automatic-speech-recognition",
model=model,
tokenizer=processor.tokenizer,
feature_extractor=processor.feature_extractor,
max_new_tokens=128,
torch_dtype=torch_dtype,
device=device,
)
whisper("c.mp3",chunk_length_s=30,stride_length_s=5,batch_size=16,return_timestamps=True,generate_kwargs = {"language":"<|th|>"})
```
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