fix train bugs and add train details
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chuxij
@@ -327,133 +327,7 @@ The `examples/input_params` directory contains sample input parameters that can
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</p>
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## 🔨 Train
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### Prerequisites
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1. Prepare the environment as described in the installation section.
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2. If you plan to train a LoRA model, install the PEFT library:
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```bash
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pip install peft
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```
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3. Prepare your dataset in Huggingface format ([Huggingface Datasets documentation](https://huggingface.co/docs/datasets/index)). The dataset should contain the following fields:
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- `keys`: Unique identifier for each audio sample
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- `filename`: Path to the audio file
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- `tags`: List of descriptive tags (e.g., `["pop", "rock"]`)
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- `norm_lyrics`: Normalized lyrics text
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- Optional fields:
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- `speaker_emb_path`: Path to speaker embedding file (use empty string if not available)
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- `recaption`: Additional tag descriptions in various formats
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Example dataset entry:
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```json
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{
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"keys": "1ce52937-cd1d-456f-967d-0f1072fcbb58",
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"filename": "data/audio/1ce52937-cd1d-456f-967d-0f1072fcbb58.wav",
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"tags": ["pop", "acoustic", "ballad", "romantic", "emotional"],
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"speaker_emb_path": "",
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"norm_lyrics": "I love you, I love you, I love you",
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"recaption": {
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"simplified": "pop",
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"expanded": "pop, acoustic, ballad, romantic, emotional",
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"descriptive": "The sound is soft and gentle, like a tender breeze on a quiet evening. It's soothing and full of longing.",
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"use_cases": "Suitable for background music in romantic films or during intimate moments.",
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"analysis": "pop, ballad, piano, guitar, slow tempo, romantic, emotional"
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}
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}
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```
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How to get an audio's reception?
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You can use `Qwen-Omini` https://chat.qwen.ai/ to describe an audio.
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Here we share the prompt we used.
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```python
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sys_prompt_without_tag = """Analyze the input audio and generate 6 description variants. Each variant must be <200 characters. Follow these exact definitions:
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1. `simplified`: Use only one most representative tag from the valid set.
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2. `expanded`: Broaden valid tags to include related sub-genres/techniques.
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3. `descriptive`: Convert tags into a sensory-rich sentence based *only on the sound*. DO NOT transcribe or reference the lyrics.
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4. `synonyms`: Replace tags with equivalent terms (e.g., 'strings' → 'orchestral').
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5. `use_cases`: Suggest practical applications based on audio characteristics.
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6. `analysis`: Analyze the audio's genre, instruments, tempo, and mood **based strictly on the audible musical elements**. Technical breakdown in specified format.
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* For the `instruments` list: **Only include instruments that are actually heard playing in the audio recording.** **Explicitly ignore any instruments merely mentioned or sung about in the lyrics.** Cover all audibly present instruments.
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7. `lyrical_rap_check`: if the audio is lyrical rap
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**Strictly ignore any information derived solely from the lyrics when performing the analysis, especially for identifying instruments.**
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**Output Format:**
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```json
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{
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"simplified": <str>,
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"expanded": <str>,
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"descriptive": <str>,
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"synonyms": <str>,
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"use_cases": <str>,
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"analysis": {
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"genre": <str list>,
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"instruments": <str list>,
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"tempo": <str>,
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"mood": <str list>
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},
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"lyrical_rap_check": <bool>
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}
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"""
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```
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### Training Parameters
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#### Common Parameters
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- `--dataset_path`: Path to your Huggingface dataset (required)
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- `--checkpoint_dir`: Directory containing the base model checkpoint
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- `--learning_rate`: Learning rate for training (default: 1e-4)
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- `--max_steps`: Maximum number of training steps (default: 2000000)
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- `--precision`: Training precision, e.g., "bf16-mixed" (default) or "fp32"
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- `--devices`: Number of GPUs to use (default: 1)
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- `--num_nodes`: Number of compute nodes to use (default: 1)
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- `--accumulate_grad_batches`: Gradient accumulation steps (default: 1)
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- `--num_workers`: Number of data loading workers (default: 8)
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- `--every_n_train_steps`: Checkpoint saving frequency (default: 2000)
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- `--every_plot_step`: Frequency of generating evaluation samples (default: 2000)
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- `--exp_name`: Experiment name for logging (default: "text2music_train_test")
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- `--logger_dir`: Directory for saving logs (default: "./exps/logs/")
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#### Base Model Training
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Train the base model with:
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```bash
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python trainer.py --dataset_path "path/to/your/dataset" --checkpoint_dir "path/to/base/checkpoint" --exp_name "your_experiment_name"
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```
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#### LoRA Training
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For LoRA training, you need to provide a LoRA configuration file:
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```bash
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python trainer.py --dataset_path "path/to/your/dataset" --checkpoint_dir "path/to/base/checkpoint" --lora_config_path "path/to/lora_config.json" --exp_name "your_lora_experiment"
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```
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Example LoRA configuration file (lora_config.json):
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```json
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{
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"r": 16,
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"lora_alpha": 32,
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"target_modules": [
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"speaker_embedder",
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"linear_q",
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"linear_k",
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"linear_v",
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"to_q",
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"to_k",
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"to_v",
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"to_out.0"
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]
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}
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```
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### Advanced Training Options
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- `--shift`: Flow matching shift parameter (default: 3.0)
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- `--gradient_clip_val`: Gradient clipping value (default: 0.5)
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- `--gradient_clip_algorithm`: Gradient clipping algorithm (default: "norm")
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- `--reload_dataloaders_every_n_epochs`: Frequency to reload dataloaders (default: 1)
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- `--val_check_interval`: Validation check interval (default: None)
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See [TRAIN_INSTRUCTION.md](./TRAIN_INSTRUCTION.md) for detailed training instructions.
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## 📜 License & Disclaimer
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