Updating XTTS docs

docs/source/models/xtts.md

@@ -39,6 +39,10 @@ You can also mail us at info@coqui.ai.
 #### 🐸TTS API
 
 ##### Single reference
+
+Splits the text into sentences and generates audio for each sentence. The audio files are then concatenated to produce the final audio.
+You can optionally disable sentence splitting for better coherence but more VRAM and possibly hitting models context length limit.
+
 ```python
 from TTS.api import TTS
 tts = TTS("tts_models/multilingual/multi-dataset/xtts_v2", gpu=True)
@@ -47,14 +51,29 @@ tts = TTS("tts_models/multilingual/multi-dataset/xtts_v2", gpu=True)
 tts.tts_to_file(text="It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent.",
                 file_path="output.wav",
                 speaker_wav=["/path/to/target/speaker.wav"],
-                language="en")
+                language="en",
+                split_sentences=True
+                )
 ```
 
 ##### Multiple references
+
+You can pass multiple audio files to the `speaker_wav` argument for better voice cloning.
+
 ```python
 from TTS.api import TTS
+
+# using the default version set in 🐸TTS
 tts = TTS("tts_models/multilingual/multi-dataset/xtts_v2", gpu=True)
 
+# using a specific version
+# 👀 see the branch names for versions on https://huggingface.co/coqui/XTTS-v2/tree/main
+# ❗some versions might be incompatible with the API
+tts = TTS("xtts_v2.0.2", gpu=True)
+
+# getting the latest XTTS_v2
+tts = TTS("xtts", gpu=True)
+
 # generate speech by cloning a voice using default settings
 tts.tts_to_file(text="It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent.",
                 file_path="output.wav",
@@ -62,6 +81,42 @@ tts.tts_to_file(text="It took me quite a long time to develop a voice, and now t
                 language="en")
 ```
 
+##### Streaming inference
+
+XTTS supports streaming inference. This is useful for real-time applications.
+
+```python
+import os
+import time
+import torch
+import torchaudio
+
+print("Loading model...")
+tts = TTS("tts_models/multilingual/multi-dataset/xtts_v2", gpu=True)
+model = tts.synthesizer.tts_model
+
+print("Computing speaker latents...")
+gpt_cond_latent, speaker_embedding = model.get_conditioning_latents(audio_path=["reference.wav"])
+
+print("Inference...")
+t0 = time.time()
+stream_generator = model.inference_stream(
+    "It took me quite a long time to develop a voice and now that I have it I am not going to be silent.",
+    "en",
+    gpt_cond_latent,
+    speaker_embedding
+)
+
+wav_chuncks = []
+for i, chunk in enumerate(stream_generator):
+    if i == 0:
+        print(f"Time to first chunck: {time.time() - t0}")
+    print(f"Received chunk {i} of audio length {chunk.shape[-1]}")
+    wav_chuncks.append(chunk)
+wav = torch.cat(wav_chuncks, dim=0)
+torchaudio.save("xtts_streaming.wav", wav.squeeze().unsqueeze(0).cpu(), 24000)
+```
+
 #### 🐸TTS Command line
 
 ##### Single reference
@@ -91,10 +146,13 @@ or for all wav files in a directory you can use:
      --use_cuda true
 ```
 
+#### 🐸TTS Model API
 
-#### model directly
+To use the model API, you need to download the model files and pass config and model file paths manually.
 
-If you want to be able to run with `use_deepspeed=True` and enjoy the speedup, you need to install deepspeed first.
+##### Calling manually
+
+If you want to be able to run with `use_deepspeed=True` and **enjoy the speedup**, you need to install deepspeed first.
 
 ```console
 pip install deepspeed==0.10.3
@@ -129,7 +187,7 @@ torchaudio.save("xtts.wav", torch.tensor(out["wav"]).unsqueeze(0), 24000)
 ```
 
 
-#### streaming inference
+##### Streaming manually
 
 Here the goal is to stream the audio as it is being generated. This is useful for real-time applications.
 Streaming inference is typically slower than regular inference, but it allows to get a first chunk of audio faster.