初始化项目，由ModelHub XC社区提供模型

Model: suzii/vi-whisper-large-v3-turbo
Source: Original Platform

README.md

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+---
+datasets:
+- capleaf/viVoice
+- NhutP/VSV-1100
+- doof-ferb/fpt_fosd
+- doof-ferb/infore1_25hours
+- google/fleurs
+- doof-ferb/LSVSC
+- quocanh34/viet_vlsp
+- linhtran92/viet_youtube_asr_corpus_v2
+- doof-ferb/infore2_audiobooks
+- linhtran92/viet_bud500
+language:
+- vi
+metrics:
+- wer
+base_model:
+- openai/whisper-large-v3-turbo
+library_name: transformers
+tags:
+- onnx
+---
+# Fine-tuned Whisper-V3-Turbo for Vietnamese ASR
+
+This project involves fine-tuning the Whisper-V3-Turbo model to improve its performance for Automatic Speech Recognition (ASR) in the Vietnamese language. The model was trained for 240 hours using a single Nvidia A6000 GPU.
+
+## Data Sources
+
+The training data comes from various Vietnamese speech corpora. Below is a list of datasets used for training:
+
+1. **capleaf/viVoice**  
+2. **NhutP/VSV-1100**  
+3. **doof-ferb/fpt_fosd**  
+4. **doof-ferb/infore1_25hours**  
+5. **google/fleurs (vi_vn)**  
+6. **doof-ferb/LSVSC**  
+7. **quocanh34/viet_vlsp**  
+8. **linhtran92/viet_youtube_asr_corpus_v2**  
+9. **doof-ferb/infore2_audiobooks**  
+10. **linhtran92/viet_bud500**
+
+## Model
+
+The model used in this project is the **Whisper-V3-Turbo**. Whisper is a multilingual ASR model trained on a large and diverse dataset. The version used here has been fine-tuned specifically for the Vietnamese language.
+
+## Training Configuration
+
+- **GPU Used**: Nvidia A6000  
+- **Training Time**: 240 hours
+- [Wandb report](https://api.wandb.ai/links/goiliace/ae0qectc)
+
+
+
+
+## Usage
+
+To use the fine-tuned model, follow the steps below:
+
+  ```python
+  import torch
+  from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
+  
+  device = "cuda:0" if torch.cuda.is_available() else "cpu"
+  torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
+  
+  model_id = "suzii/vi-whisper-large-v3-turbo-v1"
+  
+  model = AutoModelForSpeechSeq2Seq.from_pretrained(
+      model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
+  )
+  model.to(device)
+  
+  processor = AutoProcessor.from_pretrained(model_id)
+  
+  pipe = pipeline(
+      "automatic-speech-recognition",
+      model=model,
+      tokenizer=processor.tokenizer,
+      feature_extractor=processor.feature_extractor,
+      torch_dtype=torch_dtype,
+      device=device,
+  )
+  result = pipe("your-audio.mp3", return_timestamps=True)
+
+  ```
+
+## Acknowledgements
+
+This project would not be possible without the following datasets:
+
+- [capleaf/viVoice](https://huggingface.co/datasets/capleaf/viVoice)
+- [NhutP/VSV-1100](https://huggingface.co/datasets/nhutp/vsv-1100)
+- [doof-ferb/fpt_fosd](https://huggingface.co/datasets/doof-ferb/fpt_fosd)
+- [doof-ferb/infore1_25hours](https://huggingface.co/datasets/doof-ferb/infore1_25hours)
+- [google/fleurs](https://huggingface.co/datasets/google/fleurs)
+- [doof-ferb/LSVSC](https://huggingface.co/datasets/doof-ferb/LSVSC)
+- [quocanh34/viet_vlsp](https://huggingface.co/datasets/quocanh34/viet-vlsp)
+- [linhtran92/viet_youtube_asr_corpus_v2](https://huggingface.co/datasets/linhtran92/viet_youtube_asr_corpus_v2)
+- [doof-ferb/infore2_audiobooks](https://huggingface.co/datasets/doof-ferb/infore2_audiobooks/)
+- [linhtran92/viet_bud500](https://huggingface.co/datasets/linhtran92/viet_bud500)