Update MMS integration docs (#24311)

* Update mms.mdx

* Update mms.mdx

* Update docs/source/en/model_doc/mms.mdx
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Update mms.mdx

* Update docs/source/en/model_doc/mms.mdx
Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>

---------
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>

docs/source/en/model_doc/mms.mdx

@@ -30,21 +30,21 @@ for the same number of languages, as well as a language identification model for
 Experiments show that our multilingual speech recognition model more than halves the word error rate of 
 Whisper on 54 languages of the FLEURS benchmark while being trained on a small fraction of the labeled data.*
 
-Tips:
+Here are the different models open sourced in the MMS project. The models and code are originally released [here](https://github.com/facebookresearch/fairseq/tree/main/examples/mms). We have add them to the `transformers` framework, making them easier to use.
 
-- MMS is a speech model that accepts a float array corresponding to the raw waveform of the speech signal. The raw waveform should be pre-processed with [`Wav2Vec2FeatureExtractor`].
-- MMS model was trained using connectionist temporal classification (CTC) so the model output has to be decoded using
-  [`Wav2Vec2CTCTokenizer`].
-- MMS can load different language adapter weights for different languages via [`~Wav2Vec2PreTrainedModel.load_adapter`]. Language adapters only consists of roughly 2 million parameters 
-  and can therefore be efficiently loaded on the fly when needed.
+### Automatic Speech Recognition (ASR)
 
-Relevant checkpoints can be found under https://huggingface.co/models?other=mms.
+The ASR model checkpoints  can be found here : [mms-1b-fl102](https://huggingface.co/facebook/mms-1b-fl102), [mms-1b-l1107](https://huggingface.co/facebook/mms-1b-l1107), [mms-1b-all](https://huggingface.co/facebook/mms-1b-all). For best accuracy, use the `mms-1b-all` model. 
 
-MMS's architecture is based on the Wav2Vec2 model, so one can refer to [Wav2Vec2's documentation page](wav2vec2).
+Tips:
 
-The original code can be found [here](https://github.com/facebookresearch/fairseq/tree/main/examples/mms).
+- All ASR models accept a float array corresponding to the raw waveform of the speech signal. The raw waveform should be pre-processed with [`Wav2Vec2FeatureExtractor`].
+- The models were trained using connectionist temporal classification (CTC) so the model output has to be decoded using
+  [`Wav2Vec2CTCTokenizer`].
+- You can load different language adapter weights for different languages via [`~Wav2Vec2PreTrainedModel.load_adapter`]. Language adapters only consists of roughly 2 million parameters 
+  and can therefore be efficiently loaded on the fly when needed.
 
-## Loading
+#### Loading
 
 By default MMS loads adapter weights for English. If you want to load adapter weights of another language 
 make sure to specify `target_lang=<your-chosen-target-lang>` as well as `"ignore_mismatched_sizes=True`.
@@ -86,7 +86,7 @@ target_lang = "fra"
 pipe = pipeline(model=model_id, model_kwargs={"target_lang": "fra", "ignore_mismatched_sizes": True})
 ```
 
-## Inference
+#### Inference
 
 Next, let's look at how we can run MMS in inference and change adapter layers after having called [`~PretrainedModel.from_pretrained`]
 First, we load audio data in different languages using the [Datasets](https://github.com/huggingface/datasets).
@@ -156,3 +156,81 @@ processor.tokenizer.vocab.keys()
 ```
 
 to see all supported languages.
+
+To further improve performance from ASR models, language model decoding can be used. See the documentation [here](https://huggingface.co/facebook/mms-1b-all) for further details.  
+
+### Speech Synthesis (TTS)
+
+Individual TTS models are available for each of the 1100+ languages. The models and inference documentation can be found [here](https://huggingface.co/facebook/mms-tts).
+
+### Language Identification (LID)
+
+Different LID models are available based on the number of languages they can recognize - [126](https://huggingface.co/facebook/mms-lid-126), [256](https://huggingface.co/facebook/mms-lid-256), [512](https://huggingface.co/facebook/mms-lid-512), [1024](https://huggingface.co/facebook/mms-lid-1024), [2048](https://huggingface.co/facebook/mms-lid-2048), [4017](https://huggingface.co/facebook/mms-lid-4017). 
+
+#### Inference
+First, we install transformers and some other libraries
+```
+pip install torch accelerate torchaudio datasets
+pip install --upgrade transformers
+````
+pip install torch datasets[audio]
+Next, we load a couple of audio samples via `datasets`. Make sure that the audio data is sampled to 16000 kHz.
+
+```py
+from datasets import load_dataset, Audio
+
+# English
+stream_data = load_dataset("mozilla-foundation/common_voice_13_0", "en", split="test", streaming=True)
+stream_data = stream_data.cast_column("audio", Audio(sampling_rate=16000))
+en_sample = next(iter(stream_data))["audio"]["array"]
+
+# Arabic
+stream_data = load_dataset("mozilla-foundation/common_voice_13_0", "ar", split="test", streaming=True)
+stream_data = stream_data.cast_column("audio", Audio(sampling_rate=16000))
+ar_sample = next(iter(stream_data))["audio"]["array"]
+```
+
+Next, we load the model and processor
+
+```py
+from transformers import Wav2Vec2ForSequenceClassification, AutoFeatureExtractor
+import torch
+
+model_id = "facebook/mms-lid-126"
+
+processor = AutoFeatureExtractor.from_pretrained(model_id)
+model = Wav2Vec2ForSequenceClassification.from_pretrained(model_id)
+```
+
+Now we process the audio data, pass the processed audio data to the model to classify it into a language, just like we usually do for Wav2Vec2 audio classification models such as [ehcalabres/wav2vec2-lg-xlsr-en-speech-emotion-recognition](https://huggingface.co/harshit345/xlsr-wav2vec-speech-emotion-recognition)
+
+```py
+# English
+inputs = processor(en_sample, sampling_rate=16_000, return_tensors="pt")
+
+with torch.no_grad():
+    outputs = model(**inputs).logits
+
+lang_id = torch.argmax(outputs, dim=-1)[0].item()
+detected_lang = model.config.id2label[lang_id]
+# 'eng'
+
+# Arabic
+inputs = processor(ar_sample, sampling_rate=16_000, return_tensors="pt")
+
+with torch.no_grad():
+    outputs = model(**inputs).logits
+
+lang_id = torch.argmax(outputs, dim=-1)[0].item()
+detected_lang = model.config.id2label[lang_id]
+# 'ara'
+```
+
+To see all the supported languages of a checkpoint, you can print out the language ids as follows:
+```py
+processor.id2label.values()
+```
+
+### Audio Pretrained Models
+
+Pretrained models are available for two different sizes - [300M](https://huggingface.co/facebook/mms-300m) , [1Bil](https://huggingface.co/facebook/mms-1b). The architecture is based on the Wav2Vec2 model, so one can refer to [Wav2Vec2's documentation page](wav2vec2) for further details on how to finetune with models for various downstream tasks.