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*This model was published in HF papers on 2021-06-11 and contributed to Hugging Face Transformers on 2023-09-01.*


# VITS

[VITS (Variational Inference with adversarial learning for end-to-end Text-to-Speech)](https://huggingface.co/papers/2106.06103) is a end-to-end speech synthesis model, simplifying the traditional two-stage text-to-speech (TTS) systems. It's unique because it directly synthesizes speech from text using variational inference, adversarial learning, and normalizing flows to produce natural and expressive speech with diverse rhythms and intonations.

You can find all the original VITS checkpoints under the [AI at Meta](https://huggingface.co/facebook?search_models=mms-tts) organization.

> [!TIP]
> Click on the VITS models in the right sidebar for more examples of how to apply VITS.

The example below demonstrates how to generate text based on an image with [`Pipeline`] or the [`AutoModel`] class.

<hfoptions id="usage">
<hfoption id="Pipeline">

```python
from scipy.io.wavfile import write

from transformers import pipeline, set_seed


set_seed(555)

pipe = pipeline(
    task="text-to-speech",
    model="facebook/mms-tts-eng",
    device=0
)

speech = pipe("Hello, my dog is cute")

# Extract audio data and sampling rate
audio_data = speech["audio"]
sampling_rate = speech["sampling_rate"]

# Save as WAV file
write("hello.wav", sampling_rate, audio_data.squeeze())
```

</hfoption>
<hfoption id="AutoModel">

```python
import scipy
import torch
from IPython.display import Audio

from transformers import AutoTokenizer, VitsModel, set_seed


tokenizer = AutoTokenizer.from_pretrained("facebook/mms-tts-eng")
model = VitsModel.from_pretrained("facebook/mms-tts-eng", device_map="auto")
inputs = tokenizer("Hello, my dog is cute", return_tensors="pt").to(model.device)

set_seed(555)

with torch.no_grad():
    outputs = model(**inputs)

waveform = outputs.waveform[0]
scipy.io.wavfile.write("hello.wav", rate=model.config.sampling_rate, data=waveform)

# display in Colab notebook
Audio(waveform, rate=model.config.sampling_rate)
```

</hfoption>
</hfoptions>

## Notes

- Set a seed for reproducibility because VITS synthesizes speech non-deterministically.
- For languages with non-Roman alphabets (Korean, Arabic, etc.), install the [uroman](https://github.com/isi-nlp/uroman) package to preprocess the text inputs to the Roman alphabet. You can check if the tokenizer requires uroman as shown below.

   ```py
   # pip install -U uroman
   from transformers import VitsTokenizer

   tokenizer = VitsTokenizer.from_pretrained("facebook/mms-tts-eng")
   print(tokenizer.is_uroman)
   ```

   If your language requires uroman, the tokenizer automatically applies it to the text inputs. Python >= 3.10 doesn't require any additional preprocessing steps. For Python < 3.10, follow the steps below.

   ```bash
   git clone https://github.com/isi-nlp/uroman.git
   cd uroman
   export UROMAN=$(pwd)
   ```

   Create a function to preprocess the inputs. You can either use the bash variable `UROMAN` or pass the directory path directly to the function.

   ```py
   import torch
   from transformers import VitsTokenizer, VitsModel, set_seed
   import os
   import subprocess

   tokenizer = VitsTokenizer.from_pretrained("facebook/mms-tts-kor")
   model = VitsModel.from_pretrained("facebook/mms-tts-kor", device_map="auto")

   def uromanize(input_string, uroman_path):
       """Convert non-Roman strings to Roman using the `uroman` perl package."""
       script_path = os.path.join(uroman_path, "bin", "uroman.pl")

       command = ["perl", script_path]

       process = subprocess.Popen(command, stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
       # Execute the perl command
       stdout, stderr = process.communicate(input=input_string.encode())

       if process.returncode != 0:
           raise ValueError(f"Error {process.returncode}: {stderr.decode()}")

       # Return the output as a string and skip the new-line character at the end
       return stdout.decode()[:-1]

   text = "이봐 무슨 일이야"
   uromanized_text = uromanize(text, uroman_path=os.environ["UROMAN"])

   inputs = tokenizer(text=uromanized_text, return_tensors="pt").to(model.device)

   set_seed(555)  # make deterministic
   with torch.no_grad():
      outputs = model(inputs["input_ids"])

   waveform = outputs.waveform[0]
   ```

## VitsConfig

[[autodoc]] VitsConfig

## VitsTokenizer

[[autodoc]] VitsTokenizer
    - __call__
    - save_vocabulary

## VitsModel

[[autodoc]] VitsModel
    - forward
