Add FT inference example on XTTS docs

TTS/tts/models/xtts.py

@@ -794,6 +794,8 @@ def get_compatible_checkpoint_state_dict(self, model_path):
         ignore_keys = ["diffusion_decoder", "vocoder"] if self.args.use_hifigan or self.args.use_ne_hifigan else []
         ignore_keys += [] if self.args.use_hifigan else ["hifigan_decoder"]
         ignore_keys += [] if self.args.use_ne_hifigan else ["ne_hifigan_decoder"]
+        # remove xtts gpt trainer extra keys
+        ignore_keys += ["torch_mel_spectrogram_style_encoder", "torch_mel_spectrogram_dvae", "dvae"]
         for key in list(checkpoint.keys()):
             # check if it is from the coqui Trainer if so convert it
             if key.startswith("xtts."):

docs/source/models/xtts.md

@@ -16,8 +16,8 @@ a few tricks to make it faster and support streaming inference.
 Current implementation only supports inference.
 
 ### Languages
-As of now, XTTS-v1 supports 13 languages: English, Spanish, French, German, Italian, Portuguese,
-Polish, Turkish, Russian, Dutch, Czech, Arabic, and Chinese (Simplified).
+As of now, XTTS-v1.1 supports 14 languages: English, Spanish, French, German, Italian, Portuguese,
+Polish, Turkish, Russian, Dutch, Czech, Arabic, Chinese (Simplified) and Japanese.
 
 Stay tuned as we continue to add support for more languages. If you have any language requests, please feel free to reach out.
 
@@ -33,7 +33,7 @@ You can also mail us at info@coqui.ai.
 
 ```python
 from TTS.api import TTS
-tts = TTS("tts_models/multilingual/multi-dataset/xtts_v1", gpu=True)
+tts = TTS("tts_models/multilingual/multi-dataset/xtts_v1.1", 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.",
@@ -45,7 +45,7 @@ tts.tts_to_file(text="It took me quite a long time to develop a voice, and now t
 #### 🐸TTS Command line
 
 ```console
- tts --model_name tts_models/multilingual/multi-dataset/xtts_v1 \
+ tts --model_name tts_models/multilingual/multi-dataset/xtts_v1.1 \
      --text "Bugün okula gitmek istemiyorum." \
      --speaker_wav /path/to/target/speaker.wav \
      --language_idx tr \
@@ -142,6 +142,48 @@ A recipe for `XTTS_v1.1` GPT encoder training using `LJSpeech` dataset is availa
 
 You need to change the fields of the `BaseDatasetConfig` to match your dataset and then update `GPTArgs` and `GPTTrainerConfig` fields as you need. By default, it will use the same parameters that XTTS v1.1 model was trained with. To speed up the model convergence, as default, it will also download the XTTS v1.1 checkpoint and load it.
 
+After training you can do inference following the code bellow.
+
+```python
+import os
+import torch
+import torchaudio
+from TTS.tts.configs.xtts_config import XttsConfig
+from TTS.tts.models.xtts import Xtts
+
+# Add here the xtts_config path
+CONFIG_PATH = "recipes/ljspeech/xtts_v1/run/training/GPT_XTTS_LJSpeech_FT-October-23-2023_10+36AM-653f2e75/config.json"
+# Add here the vocab file that you have used to train the model
+TOKENIZER_PATH = "recipes/ljspeech/xtts_v1/run/training/XTTS_v1.1_original_model_files/vocab.json"
+# Add here the checkpoint that you want to do inference with
+XTTS_CHECKPOINT = "recipes/ljspeech/xtts_v1/run/training/GPT_XTTS_LJSpeech_FT/best_model.pth"
+# Add here the speaker reference
+SPEAKER_REFERENCE = "LjSpeech_reference.wav"
+
+# output wav path
+OUTPUT_WAV_PATH = "xtts-ft.wav"
+
+print("Loading model...")
+config = XttsConfig()
+config.load_json(CONFIG_PATH)
+model = Xtts.init_from_config(config)
+model.load_checkpoint(config, checkpoint_path=XTTS_CHECKPOINT, vocab_path=TOKENIZER_PATH, use_deepspeed=False)
+model.cuda()
+
+print("Computing speaker latents...")
+gpt_cond_latent, diffusion_conditioning, speaker_embedding = model.get_conditioning_latents(audio_path=SPEAKER_REFERENCE)
+
+print("Inference...")
+out = model.inference(
+    "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,
+    diffusion_conditioning,
+    temperature=0.7, # Add custom parameters here
+)
+torchaudio.save(OUTPUT_WAV_PATH, torch.tensor(out["wav"]).unsqueeze(0), 24000)
+```
 
 
 ## Important resources & papers