Datasets:

amir0907
/

datavibe

It is recommended to install this in a fresh environment. Specifically, the Dockerized environment runpod/pytorch:2.8.0-py3.11-cuda12.8.1-cudnn-devel-ubuntu22.04 has been tested to work.

Transformers version 4.51.3 is known to work, while other versions have errors related to Qwen2 architecture.

git clone https://github.com/voicepowered-ai/VibeVoice-finetuning

pip install -e .

pip uninstall -y transformers && pip install transformers==4.51.3

(OPTIONAL) wandb login

(OPTIONAL) export HF_HOME=/workspace/hf_models

Usage

VibeVoice 1.5B / 7B (LoRA) fine-tuning

We put some code together for training VibeVoice (7B) with LoRA. This uses the vendored VibeVoice model/processor and trains with a dual loss: masked CE on text tokens plus diffusion MSE on acoustic latents.

Requirements:

Download a compatible VibeVoice 7B or 1.5b checkpoint (config + weights) and its processor files (preprocessor_config.json) or run straight from HF model.
A 24khz audio dataset with audio files (target audio), text prompts (transcriptions) and optionally voice prompts (reference audio)

Training with Hugging Face Dataset

python -m src.finetune_vibevoice_lora \

--model_name_or_path aoi-ot/VibeVoice-Large \

--processor_name_or_path src/vibevoice/processor \

--dataset_name your/dataset \

--text_column_name text \

--audio_column_name audio \

--voice_prompts_column_name voice_prompts \

--output_dir outputTrain3 \

--per_device_train_batch_size 8 \

--gradient_accumulation_steps 16 \

--learning_rate 2.5e-5 \

--num_train_epochs 5 \

--logging_steps 10 \

--save_steps 100 \

--eval_steps 100 \

--report_to wandb \

--remove_unused_columns False \

--bf16 True \

--do_train \

--gradient_clipping \

--gradient_checkpointing False \

--ddpm_batch_mul 4 \

--diffusion_loss_weight 1.4 \

--train_diffusion_head True \

--ce_loss_weight 0.04 \

--voice_prompt_drop_rate 0.2 \

--lora_target_modules q_proj,k_proj,v_proj,o_proj,gate_proj,up_proj,down_proj \

--lr_scheduler_type cosine \

--warmup_ratio 0.03 \

--max_grad_norm 0.8

Training with Local JSONL Dataset

python -m src.finetune_vibevoice_lora \

--model_name_or_path aoi-ot/VibeVoice-Large \

--processor_name_or_path src/vibevoice/processor \

--train_jsonl prompts.jsonl \

--text_column_name text \

--audio_column_name audio \

--output_dir outputTrain3 \

--per_device_train_batch_size 8 \

--gradient_accumulation_steps 16 \

--learning_rate 2.5e-5 \

--num_train_epochs 5 \

--logging_steps 10 \

--save_steps 100 \

--report_to wandb \

--remove_unused_columns False \

--bf16 True \

--do_train \

--gradient_clipping \

--gradient_checkpointing False \

--ddpm_batch_mul 4 \

--diffusion_loss_weight 1.4 \

--train_diffusion_head True \

--ce_loss_weight 0.04 \

--voice_prompt_drop_rate 0.2 \

--lora_target_modules q_proj,k_proj,v_proj,o_proj,gate_proj,up_proj,down_proj \

--lr_scheduler_type cosine \

--warmup_ratio 0.03 \

--max_grad_norm 0.8

JSONL format:

You can provide an optional voice_prompts key. If it is omitted, a voice prompt will be automatically generated from the target audio.

Example without a pre-defined voice prompt (will be auto-generated):

{"text": "Speaker 0: Speaker0 transcription.", "audio": "/workspace/wavs/segment_000000.wav"}

Example with a pre-defined voice prompt:

{"text": "Speaker 0: Speaker0 transcription.", "audio": "/workspace/wavs/segment_000000.wav", "voice_prompts": "/path/to/a/different/prompt.wav"}

Example with multiple speakers and voice prompts:

{"text": "Speaker 0: How is the project coming along?\nSpeaker 1: It's going well, we should be finished by Friday.", "audio": "/data/conversations/convo_01.wav", "voice_prompts": ["/data/prompts/alice_voice_prompt.wav", "/data/prompts/bob_voice_prompt.wav"]}

Notes:

Audio is assumed to be 24 kHz; input audio will be loaded/resampled to 24 kHz.
If you pass raw NumPy arrays or torch Tensors as audio (without sampling rate metadata), the collator assumes they are already 24 kHz. To trigger resampling, provide dicts like {"array": <np.ndarray>, "sampling_rate": } or file paths.
Tokenizers (acoustic/semantic) are frozen by default. LoRA is applied to the LLM (Qwen) and optionally to the diffusion head.
The collator builds interleaved sequences with speech placeholders and computes the required masks for diffusion loss.
If a voice_prompts column is not provided in your dataset for a given sample, a voice prompt is automatically generated by taking a random clip from the target audio. This fallback ensures the model's voice cloning ability is maintained. You can override this behavior by providing your own voice prompts.
Said voice_prompts are randomly dropped during training to improve generalization. Drop rates of 0.2 and 0.25 have been tested with satisfactory results.
The model learns to emit a closing [speech_end] token after target placeholders.
For multi‑speaker prompts, ensure voice_prompts list order matches Speaker 0/1/... tags in your text.
LoRA adapters are saved under output_dir/lora after training.

Acknowledgements

Training Script Arguments

Comprehensive list of all the command-line arguments available for the fine-tuning script.

Model & Architecture Arguments

Controls the base model, its configuration, and which components are trained.

--model_name_or_path
What it does: Specifies the path to the pretrained VibeVoice base model. This can be a local directory or a Hugging Face Hub repository ID.
Required: Yes.
Example:


--model_name_or_path aoi-ot/VibeVoice-Large

--processor_name_or_path
What it does: Specifies the path to the VibeVoice processor configuration. If not provided, it defaults to the model_name_or_path.
Example:


--processor_name_or_path src/vibevoice/processor

--train_diffusion_head
What it does: A boolean flag to enable full fine-tuning of the diffusion prediction head. When enabled, all parameters of the diffusion head become trainable.
Example:


--train_diffusion_head True

--train_connectors
What it does: A boolean flag to enable training of the acoustic and semantic connectors, which bridge different parts of the model.
Example:


--train_connectors True

--lora_target_modules
What it does: A comma-separated string of module names within the language model to apply LoRA adapters to. This is the primary way to enable LoRA for the text-processing part of the model.
Example:


--lora_target_modules q_proj,k_proj,v_proj,o_proj,gate_proj,up_proj,down_proj

--lora_r
What it does: The rank (r) of the LoRA decomposition. A smaller number means fewer trainable parameters.
Default: 8
Example:


--lora_r 16

--lora_alpha
What it does: The scaling factor for the LoRA weights. A common practice is to set lora_alpha to be four times the value of lora_r.
Default: 32
Example:


--lora_alpha 64

--lora_wrap_diffusion_head
What it does: An alternative to --train_diffusion_head. If True, it applies LoRA adapters to the diffusion head instead of fine-tuning it fully, enabling more parameter-efficient training of the head. Must only use --train_diffusion_head or --lora_wrap_diffusion_head
Default: False
--layers_to_freeze
What it does: Comma-separated indices of diffusion head layers to freeze (e.g., '0,1,5,7,8'). Diffusion head layer indices
Default: None

Data & Processing Arguments

Defines the dataset to be used, its structure, and how it should be processed.

--train_jsonl
What it does: Path to your local training data file in JSONL (JSON Lines) format. Each line should be a JSON object with keys for text and audio path.
Example:


--train_jsonl prompts.jsonl

--validation_jsonl
What it does: Optional path to a local validation data file in JSONL format.
Example:


--validation_jsonl validation_prompts.jsonl

--text_column_name
What it does: The name of the key in your JSONL file that contains the text transcription/prompt.
Default: text
Example:


--text_column_name "prompt"

--audio_column_name
What it does: The name of the key in your JSONL file that contains the path to the audio file.
Default: audio
Example:


--audio_column_name "file_path"

--voice_prompt_drop_rate
What it does: The probability (from 0.0 to 1.0) of randomly dropping the conditioning voice prompt during training. This acts as a regularizer.
Default: 0.0
Example:


--voice_prompt_drop_rate 0.2

Core Training Arguments

Standard Hugging Face TrainingArguments that control the training loop, optimizer, and saving.

--output_dir
What it does: The directory where model checkpoints and final outputs will be saved.
Required: Yes.
Example:


--output_dir output_model

--per_device_train_batch_size
What it does: The number of training examples processed per GPU in a single step.
Example:


--per_device_train_batch_size 8

--gradient_accumulation_steps
What it does: The number of forward passes to accumulate gradients for before performing an optimizer step. This effectively increases the batch size without using more VRAM.
Example:


--gradient_accumulation_steps 16

--learning_rate
What it does: The initial learning rate for the optimizer.
Example:


--learning_rate 2.5e-5

--num_train_epochs
What it does: The total number of times to iterate over the entire training dataset.
Example:


--num_train_epochs 5

--logging_steps
What it does: How often (in steps) to log training metrics like loss.
Example:


--logging_steps 10

--save_steps
What it does: How often (in steps) to save a model checkpoint.
Example:


--save_steps 100

--report_to
What it does: The integration to report logs to. Can be wandb, tensorboard, or none.
Example:


--report_to wandb

--remove_unused_columns
What it does: Whether to remove columns from the dataset not used by the model's forward method. This must be set to False for this script to work correctly.
Example:


--remove_unused_columns False

--bf16
What it does: Enables mixed-precision training using bfloat16. This speeds up training and reduces memory usage on compatible GPUs (NVIDIA Ampere series and newer).
Example:


--bf16 True

--gradient_checkpointing
What it does: A memory-saving technique that trades compute for memory. Useful for training large models on limited VRAM.
Example:


--gradient_checkpointing True

--lr_scheduler_type
What it does: The type of learning rate schedule to use (e.g., linear, cosine, constant).
Example:


--lr_scheduler_type cosine

--warmup_ratio
What it does: The proportion of total training steps used for a linear warmup from 0 to the initial learning rate.
Example:


--warmup_ratio 0.03

Custom VibeVoice Training Arguments

Special arguments to control VibeVoice-specific training behaviors.

--gradient_clipping
What it does: A custom boolean flag that acts as the master switch for gradient clipping. If you include this flag, the value from --max_grad_norm will be used to prevent exploding gradients.
Example:


--gradient_clipping

--max_grad_norm
What it does: The maximum value for gradient clipping. Only active if --gradient_clipping is also used.
Default: 1.0
Example:


--max_grad_norm 0.8

--diffusion_loss_weight
What it does: A float that scales the importance of the diffusion loss (for speech generation quality) in the total loss calculation.
Example:


--diffusion_loss_weight 1.4

--ce_loss_weight
What it does: A float that scales the importance of the Cross-Entropy loss (for text prediction accuracy) in the total loss calculation.
Example:


--ce_loss_weight 0.04

--ddpm_batch_mul
What it does: An integer multiplier for the batch size used specifically within the diffusion process.
Example:


--ddpm_batch_mul 4