[MMS] Update docs with HF TTS implementation (#25907)

* [MMS] Update docs with HF TTS implementation

* Update docs/source/en/model_doc/mms.md

Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

* add uromanise to docs

---------

Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

docs/source/en/model_doc/vits.md

@@ -98,6 +98,54 @@ print(tokenizer.is_uroman)
 If required, you should apply the uroman package to your text inputs **prior** to passing them to the `VitsTokenizer`, 
 since currently the tokenizer does not support performing the pre-processing itself.  
 
+To do this, first clone the uroman repository to your local machine and set the bash variable `UROMAN` to the local path:
+
+```bash
+git clone https://github.com/isi-nlp/uroman.git
+cd uroman
+export UROMAN=$(pwd)
+```
+
+You can then pre-process the text input using the following code snippet. You can either rely on using the bash variable 
+`UROMAN` to point to the uroman repository, or you can pass the uroman directory as an argument to the `uromaize` function:
+
+```python
+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")
+
+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"
+uromaized_text = uromanize(text, uroman_path=os.environ["UROMAN"])
+
+inputs = tokenizer(text=uromaized_text, return_tensors="pt")
+
+set_seed(555)  # make deterministic
+with torch.no_grad():
+   outputs = model(inputs["input_ids"])
+
+waveform = outputs.waveform[0]
+```
+
 ## VitsConfig
 
 [[autodoc]] VitsConfig