enginex-mlu370-any2any/transformers/docs/source/en/model_doc/dia.md

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*This model was released on 2025-04-21 and added to Hugging Face Transformers on 2025-06-26.*

# Dia

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## Overview

[Dia](https://github.com/nari-labs/dia) is an open-source text-to-speech (TTS) model (1.6B parameters) developed by [Nari Labs](https://huggingface.co/nari-labs).
It can generate highly realistic dialogue from transcript including non-verbal communications such as laughter and coughing.
Furthermore, emotion and tone control is also possible via audio conditioning (voice cloning).

**Model Architecture:**
Dia is an encoder-decoder transformer based on the original transformer architecture. However, some more modern features such as
rotational positional embeddings (RoPE) are also included. For its text portion (encoder), a byte tokenizer is utilized while
for the audio portion (decoder), a pretrained codec model [DAC](./dac) is used - DAC encodes speech into discrete codebook
tokens and decodes them back into audio.

## Usage Tips

### Generation with Text

```python
from transformers import AutoProcessor, DiaForConditionalGeneration, infer_device

torch_device = infer_device()
model_checkpoint = "nari-labs/Dia-1.6B-0626"

text = ["[S1] Dia is an open weights text to dialogue model."]
processor = AutoProcessor.from_pretrained(model_checkpoint)
inputs = processor(text=text, padding=True, return_tensors="pt").to(torch_device)

model = DiaForConditionalGeneration.from_pretrained(model_checkpoint).to(torch_device)
outputs = model.generate(**inputs, max_new_tokens=256)  # corresponds to around ~2s

# save audio to a file
outputs = processor.batch_decode(outputs)
processor.save_audio(outputs, "example.wav")

```

### Generation with Text and Audio (Voice Cloning)

```python
from datasets import load_dataset, Audio
from transformers import AutoProcessor, DiaForConditionalGeneration, infer_device

torch_device = infer_device()
model_checkpoint = "nari-labs/Dia-1.6B-0626"

ds = load_dataset("hf-internal-testing/dailytalk-dummy", split="train")
ds = ds.cast_column("audio", Audio(sampling_rate=44100))
audio = ds[-1]["audio"]["array"]
# text is a transcript of the audio + additional text you want as new audio
text = ["[S1] I know. It's going to save me a lot of money, I hope. [S2] I sure hope so for you."]

processor = AutoProcessor.from_pretrained(model_checkpoint)
inputs = processor(text=text, audio=audio, padding=True, return_tensors="pt").to(torch_device)
prompt_len = processor.get_audio_prompt_len(inputs["decoder_attention_mask"])

model = DiaForConditionalGeneration.from_pretrained(model_checkpoint).to(torch_device)
outputs = model.generate(**inputs, max_new_tokens=256)  # corresponds to around ~2s

# retrieve actually generated audio and save to a file
outputs = processor.batch_decode(outputs, audio_prompt_len=prompt_len)
processor.save_audio(outputs, "example_with_audio.wav")
```

### Training

```python
from datasets import load_dataset, Audio
from transformers import AutoProcessor, DiaForConditionalGeneration, infer_device

torch_device = infer_device()
model_checkpoint = "nari-labs/Dia-1.6B-0626"

ds = load_dataset("hf-internal-testing/dailytalk-dummy", split="train")
ds = ds.cast_column("audio", Audio(sampling_rate=44100))
audio = ds[-1]["audio"]["array"]
# text is a transcript of the audio
text = ["[S1] I know. It's going to save me a lot of money, I hope."]

processor = AutoProcessor.from_pretrained(model_checkpoint)
inputs = processor(
    text=text,
    audio=audio,
    generation=False,
    output_labels=True,
    padding=True,
    return_tensors="pt"
).to(torch_device)

model = DiaForConditionalGeneration.from_pretrained(model_checkpoint).to(torch_device)
out = model(**inputs)
out.loss.backward()
```

This model was contributed by [Jaeyong Sung](https://huggingface.co/buttercrab), [Arthur Zucker](https://huggingface.co/ArthurZ),
and [Anton Vlasjuk](https://huggingface.co/AntonV). The original code can be found [here](https://github.com/nari-labs/dia/).

## DiaConfig

[[autodoc]] DiaConfig

## DiaDecoderConfig

[[autodoc]] DiaDecoderConfig

## DiaEncoderConfig

[[autodoc]] DiaEncoderConfig

## DiaTokenizer

[[autodoc]] DiaTokenizer
    - __call__

## DiaFeatureExtractor

[[autodoc]] DiaFeatureExtractor
    - __call__

## DiaProcessor

[[autodoc]] DiaProcessor
    - __call__
    - batch_decode
    - decode

## DiaModel

[[autodoc]] DiaModel
    - forward

## DiaForConditionalGeneration

[[autodoc]] DiaForConditionalGeneration
    - forward
    - generate