LakoreAI
/

whisper-small-vi-lora

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 ---
+language:
+- vi
+license: apache-2.0
+base_model: openai/whisper-small
+tags:
+- whisper
+- asr
+- vietnamese
+- peft
+- lora
+- audio
+datasets:
+- mozilla-foundation/common_voice_11_0
+metrics:
+- wer
+pipeline_tag: automatic-speech-recognition
 ---
+# Whisper Small — Vietnamese (LoRA Fine-tuned)
+Fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on Vietnamese speech using LoRA adapters and the Mozilla Common Voice 11 dataset.
+## Training Results
+| Metric | Value |
+|---|---|
+| Training Loss | 0.9382 |
+| Epochs | 5 |
+| Global Steps | 470 |
+| Samples/sec | 7.37 |
+| Total FLOPs | 4.60e+18 |
 ## Model Details
+- **Base model:** `openai/whisper-small` (244M params)
+- **Method:** LoRA (Low-Rank Adaptation)
+- **Trainable params:** ~13M (5.09% of base)
+- **Target modules:** `q_proj`, `v_proj`, `k_proj`, `out_proj`, `fc1`, `fc2`
+- **LoRA rank:** 32 · **alpha:** 64 · **dropout:** 0.05
+- **Language:** Vietnamese (`vi`)
+- **Task:** Transcription
 ## Training Details
+- **Dataset:** Mozilla Common Voice 11.0 (`vi`)
+- **Learning rate:** 1e-4 with linear warmup (500 steps)
+- **Batch size:** 8 × 2 gradient accumulation = effective 16
+- **Precision:** FP16
+- **Framework:** 🤗 Transformers + PEFT
+**Data augmentation applied:**
+- Speed perturbation ±10% (p=0.3)
+- Additive Gaussian noise (p=0.3)
+## Usage
+```python
+from peft import PeftModel
+from transformers import WhisperForConditionalGeneration, WhisperProcessor
+import torch
+base = WhisperForConditionalGeneration.from_pretrained("openai/whisper-small")
+model = PeftModel.from_pretrained(base, "LakoreAI/whisper-small-vi-lora")
+processor = WhisperProcessor.from_pretrained("LakoreAI/whisper-small-vi-lora")
+# Optional: merge LoRA for faster inference
+model = model.merge_and_unload()
+model.eval()
+# Inference
+def transcribe(audio_array, sampling_rate=16000):
+    inputs = processor(audio_array, sampling_rate=sampling_rate, return_tensors="pt")
+    with torch.no_grad():
+        ids = model.generate(
+            inputs.input_features,
+            language="vietnamese",
+            task="transcribe",
+            max_new_tokens=225,
+        )
+    return processor.tokenizer.decode(ids[0], skip_special_tokens=True)
+```
+## Limitations
+- Optimized for Vietnamese only; other languages will degrade significantly
+- Common Voice data skews toward read speech; spontaneous/accented speech may perform worse
+- Short clips (<1s) or clipped audio may cause hallucinations