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Model: namphungdn134/whisper-small-vi
Source: Original Platform

README.md

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+---
+library_name: transformers
+language:
+- vi
+license: mit
+base_model: openai/whisper-small
+tags:
+- generated_from_trainer
+- Speech_to_text
+- audio2text
+- S2T
+- STT
+
+metrics:
+- wer
+model-index:
+- name: Whisper Small Vi - Nam Phung
+  results: []
+pipeline_tag: automatic-speech-recognition
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+
+# Whisper Small Vi V1.1: Whisper Small for Vietnamese Fine-Tuned by Nam Phung 🚀
+
+## 📝 Introduction
+
+This is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) model on Vietnamese speech data. The model aims to improve transcription accuracy and robustness for Vietnamese automatic speech recognition (ASR) tasks, especially in real-world scenarios.
+
+## 📊 Fine-tuning Results
+
+- **Word Error Rate (WER)**: 9.3485
+<!-- training_steps: 50000 -->
+
+> Evaluation was performed on a held-out test set with diverse regional accents and speaking styles.
+
+## 📝 Model Description
+
+The Whisper small model is a transformer-small sequence-to-sequence model designed for automatic speech recognition and translation tasks. It has been trained on over 680,000 hours of labeled audio data in multiple languages. The fine-tuned version of this model focuses on the Vietnamese language, aiming to improve transcription accuracy and handling of local dialects.
+
+This model works with the WhisperProcessor to pre-process audio inputs into log-Mel spectrograms and decode them into text.
+
+## 📁 Dataset
+
+- Total Duration: More 250 hours of high-quality Vietnamese speech data
+- Sources: Public Vietnamese datasets
+- Format: 16kHz WAV files with corresponding text transcripts  
+- Preprocessing: Audio was normalized and segmented. Transcripts were cleaned and tokenized.  
+
+## 🚀 How to Use
+
+To use the fine-tuned model, you can follow these steps:
+
+1. Install the required dependencies:
+   ```python
+   # Install required libraries
+   !pip install transformers torch librosa soundfile --quiet
+
+   # Import necessary libraries
+   import torch
+   import librosa
+   import soundfile as sf
+   from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
+
+   print("Environment setup completed!")
+   ```
+
+2. Use the model for inference:
+   ```python
+   import torch
+   import librosa
+   from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
+
+   device = "cuda" if torch.cuda.is_available() else "cpu"
+   print(f"Using device: {device}")
+
+   # Load processor and model
+   model_id = "namphungdn134/whisper-small-vi"
+   print(f"Loading model from: {model_id}")
+   processor = AutoProcessor.from_pretrained(model_id)
+   model = AutoModelForSpeechSeq2Seq.from_pretrained(model_id).to(device)
+
+   # config language and task
+   forced_decoder_ids = processor.get_decoder_prompt_ids(language="vi", task="transcribe")
+   model.config.forced_decoder_ids = forced_decoder_ids
+   print(f"Forced decoder IDs for Vietnamese: {forced_decoder_ids}")
+
+   # Preprocess
+   audio_path = "example.wav"  
+   print(f"Loading audio from: {audio_path}")
+   audio, sr = librosa.load(audio_path, sr=16000)  
+   input_features = processor(audio, sampling_rate=16000, return_tensors="pt").input_features.to(device)
+   print(f"Input features shape: {input_features.shape}")
+
+   # Generate
+   print("Generating transcription...")
+   with torch.no_grad():
+      predicted_ids = model.generate(input_features, forced_decoder_ids=forced_decoder_ids)
+
+   transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)[0]
+   print("📝 Transcription:", transcription)
+
+   # Debug: Print token to check
+   print("Predicted IDs:", predicted_ids[0].tolist())
+   ```
+
+## ⚠️ Limitations
+
+- This model is specifically fine-tuned for the Vietnamese language. It might not perform well on other languages.
+- Struggles with overlapping speech or noisy background.
+- Performance may drop with strong dialectal variations not well represented in training data.
+
+## 📄 License
+
+This model is licensed under the [MIT License](LICENSE).
+
+## 📚 Citation
+
+If you use this model in your research or application, please cite the original Whisper model and this fine-tuning work as follows:
+
+```
+@article{Whisper2021,
+  title={Whisper: A Multilingual Speech Recognition Model},
+  author={OpenAI},
+  year={2021},
+  journal={arXiv:2202.12064},
+  url={https://arxiv.org/abs/2202.12064}
+}
+```
+
+```
+@misc{title={Whisper small Vi V1.1 - Nam Phung},
+  author={Nam Phùng},
+  organization={DUT},
+  year={2025},
+  url={https://huggingface.co/namphungdn134/whisper-small-vi},
+  url={https://github.com/namphung134/ASR-Vietnamese}
+}
+```
+
+---
+
+## 📬 Contact 
+
+For questions, collaborations, or suggestions, feel free to reach out via [namphungdn134@gmail.com].

config.json

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+{
+  "activation_dropout": 0.0,
+  "activation_function": "gelu",
+  "apply_spec_augment": false,
+  "architectures": [
+    "WhisperForConditionalGeneration"
+  ],
+  "attention_dropout": 0.0,
+  "begin_suppress_tokens": null,
+  "bos_token_id": 50257,
+  "classifier_proj_size": 256,
+  "d_model": 768,
+  "decoder_attention_heads": 12,
+  "decoder_ffn_dim": 3072,
+  "decoder_layerdrop": 0.0,
+  "decoder_layers": 12,
+  "decoder_start_token_id": 50258,
+  "dropout": 0.0,
+  "encoder_attention_heads": 12,
+  "encoder_ffn_dim": 3072,
+  "encoder_layerdrop": 0.0,
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+  "eos_token_id": 50257,
+  "forced_decoder_ids": [
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+  "mask_feature_length": 10,
+  "mask_feature_min_masks": 0,
+  "mask_feature_prob": 0.0,
+  "mask_time_length": 10,
+  "mask_time_min_masks": 2,
+  "mask_time_prob": 0.05,
+  "max_length": null,
+  "max_source_positions": 1500,
+  "max_target_positions": 448,
+  "median_filter_width": 7,
+  "model_type": "whisper",
+  "num_hidden_layers": 12,
+  "num_mel_bins": 80,
+  "pad_token_id": 50257,
+  "scale_embedding": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.51.3",
+  "use_cache": true,
+  "use_weighted_layer_sum": false,
+  "vocab_size": 51865
+}

generation_config.json

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+}

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preprocessor_config.json

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+  "sampling_rate": 16000
+}

special_tokens_map.json

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