Initial commit: FunASR Speech Recognition Toolkit

Add complete FunASR codebase including models, runtime, and documentation.

examples/industrial_data_pretraining/whisper/README.md

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+# Whisper Fine-tuning with FunASR
+
+Fine-tune OpenAI Whisper models on your own data using FunASR's training framework.
+
+## Supported Models
+
+- whisper-tiny / whisper-tiny.en
+- whisper-base / whisper-base.en
+- whisper-small / whisper-small.en
+- whisper-medium / whisper-medium.en
+- whisper-large-v1 / whisper-large-v2 / whisper-large-v3 / whisper-large-v3-turbo
+
+## Data Preparation
+
+Prepare data in JSONL format:
+
+```json
+{"key": "utt001", "source": "/path/to/audio1.wav", "target": "the transcription text"}
+{"key": "utt002", "source": "/path/to/audio2.wav", "target": "another transcription"}
+```
+
+## Fine-tuning
+
+```bash
+bash finetune.sh
+```
+
+Or customize directly:
+
+```python
+from funasr import AutoModel
+
+model = AutoModel(model="Whisper-large-v3", model_conf={"hub": "openai"})
+
+# Training uses the forward() method which computes cross-entropy loss
+# on (mel-spectrogram, token_ids) pairs
+```
+
+## Key Parameters
+
+| Parameter | Default | Description |
+|-----------|---------|-------------|
+| model | Whisper-large-v3 | Model size |
+| lr | 1e-5 | Learning rate (lower for larger models) |
+| max_epoch | 10 | Training epochs |
+| batch_size | 4 | Per-GPU batch size |
+| warmup_steps | 500 | LR warmup |
+
+## Tips
+
+- For Chinese fine-tuning, use `whisper-large-v3` (best multilingual base)
+- Freeze encoder for faster training: add `++train_conf.freeze_param="model.encoder"`
+- Use smaller learning rates (1e-5 ~ 5e-6) to avoid catastrophic forgetting
+- Recommended: 100+ hours of target-domain audio for meaningful improvement
+
+## After Fine-tuning
+
+```python
+from funasr import AutoModel
+
+# Load fine-tuned model
+model = AutoModel(model="/path/to/exp/whisper_finetune")
+result = model.generate(input="test.wav")
+print(result[0]["text"])
+```