Add lang code in XTTS doc

TTS/tts/configs/xtts_config.py

@@ -37,29 +37,11 @@ class XttsConfig(BaseTTSConfig):
             If set to float < 1, only the smallest set of most probable tokens with probabilities that add up to top_p or higher are kept for generation.
             Defaults to `0.8`.
 
-        cond_free_k (float):
-            Knob that determines how to balance the conditioning free signal with the conditioning-present signal. [0,inf].
-            As cond_free_k increases, the output becomes dominated by the conditioning-free signal.
-            Formula is: output=cond_present_output*(cond_free_k+1)-cond_absenct_output*cond_free_k. Defaults to `2.0`.
-
-        diffusion_temperature (float):
-            Controls the variance of the noise fed into the diffusion model. [0,1]. Values at 0
-            are the "mean" prediction of the diffusion network and will sound bland and smeared.
-            Defaults to `1.0`.
-
         num_gpt_outputs (int):
             Number of samples taken from the autoregressive model, all of which are filtered using CLVP.
             As XTTS is a probabilistic model, more samples means a higher probability of creating something "great".
             Defaults to `16`.
 
-        decoder_iterations (int):
-            Number of diffusion steps to perform. [0,4000]. More steps means the network has more chances to iteratively refine
-            the output, which should theoretically mean a higher quality output. Generally a value above 250 is not noticeably better,
-            however. Defaults to `30`.
-
-        decoder_sampler (str):
-            Diffusion sampler to be used. `ddim` or `dpm++2m`. Defaults to `ddim`.
-
         gpt_cond_len (int):
             Secs audio to be used as conditioning for the autoregressive model. Defaults to `3`.
 
@@ -110,11 +92,7 @@ class XttsConfig(BaseTTSConfig):
     repetition_penalty: float = 2.0
     top_k: int = 50
     top_p: float = 0.85
-    cond_free_k: float = 2.0
-    diffusion_temperature: float = 1.0
     num_gpt_outputs: int = 1
-    decoder_iterations: int = 30
-    decoder_sampler: str = "ddim"
 
     # cloning
     gpt_cond_len: int = 3

TTS/tts/models/xtts.py

@@ -152,19 +152,6 @@ class XttsArgs(Coqpit):
         gpt_code_stride_len (int, optional): The hop_size of dvae and consequently of the gpt output. Defaults to 1024.
         gpt_use_masking_gt_prompt_approach (bool, optional):  If True, it will use ground truth as prompt and it will mask the loss to avoid repetition. Defaults to True.
         gpt_use_perceiver_resampler (bool, optional):  If True, it will use perceiver resampler from flamingo paper - https://arxiv.org/abs/2204.14198. Defaults to False.
-
-        For DiffTTS model:
-        diff_model_channels (int, optional): The number of channels for the DiffTTS model. Defaults to 1024.
-        diff_num_layers (int, optional): The number of layers for the DiffTTS model. Defaults to 10.
-        diff_in_channels (int, optional): The input channels for the DiffTTS model. Defaults to 100.
-        diff_out_channels (int, optional): The output channels for the DiffTTS model. Defaults to 200.
-        diff_in_latent_channels (int, optional): The input latent channels for the DiffTTS model. Defaults to 1024.
-        diff_in_tokens (int, optional): The input tokens for the DiffTTS model. Defaults to 8193.
-        diff_dropout (int, optional): The dropout percentage for the DiffTTS model. Defaults to 0.
-        diff_use_fp16 (bool, optional): Whether to use fp16 for the DiffTTS model. Defaults to False.
-        diff_num_heads (int, optional): The number of heads for the DiffTTS model. Defaults to 16.
-        diff_layer_drop (int, optional): The layer dropout percentage for the DiffTTS model. Defaults to 0.
-        diff_unconditioned_percentage (int, optional): The percentage of unconditioned inputs for the DiffTTS model. Defaults to 0.
     """
 
     gpt_batch_size: int = 1
@@ -193,19 +180,6 @@ class XttsArgs(Coqpit):
     gpt_use_masking_gt_prompt_approach: bool = True
     gpt_use_perceiver_resampler: bool = False
 
-    # Diffusion Decoder params
-    diff_model_channels: int = 1024
-    diff_num_layers: int = 10
-    diff_in_channels: int = 100
-    diff_out_channels: int = 200
-    diff_in_latent_channels: int = 1024
-    diff_in_tokens: int = 8193
-    diff_dropout: int = 0
-    diff_use_fp16: bool = False
-    diff_num_heads: int = 16
-    diff_layer_drop: int = 0
-    diff_unconditioned_percentage: int = 0
-
     # HifiGAN Decoder params
     input_sample_rate: int = 22050
     output_sample_rate: int = 24000
@@ -426,10 +400,6 @@ def inference_with_config(self, text, config, ref_audio_path, language, **kwargs
             "repetition_penalty": config.repetition_penalty,
             "top_k": config.top_k,
             "top_p": config.top_p,
-            "cond_free_k": config.cond_free_k,
-            "diffusion_temperature": config.diffusion_temperature,
-            "decoder_iterations": config.decoder_iterations,
-            "decoder_sampler": config.decoder_sampler,
             "gpt_cond_len": config.gpt_cond_len,
             "max_ref_len": config.max_ref_len,
             "sound_norm_refs": config.sound_norm_refs,
@@ -454,13 +424,6 @@ def full_inference(
         gpt_cond_len=6,
         max_ref_len=10,
         sound_norm_refs=False,
-        # Decoder inference
-        decoder_iterations=100,
-        cond_free=True,
-        cond_free_k=2,
-        diffusion_temperature=1.0,
-        decoder_sampler="ddim",
-        decoder="hifigan",
         **hf_generate_kwargs,
     ):
         """

docs/source/models/xtts.md

@@ -24,8 +24,7 @@ a few tricks to make it faster and support streaming inference.
 Current implementation only supports inference.
 
 ### Languages
-As of now, XTTS-v2 supports 16 languages: English, Spanish, French, German, Italian, Portuguese,
-Polish, Turkish, Russian, Dutch, Czech, Arabic, Chinese (Simplified), Japanese, Hungarian, Korean
+As of now, XTTS-v2 supports 16 languages: English (en), Spanish (es), French (fr), German (de), Italian (it), Portuguese (pt), Polish (pl), Turkish (tr), Russian (ru), Dutch (nl), Czech (cs), Arabic (ar), Chinese (zh-cn), Japanese (ja), Hungarian (hu) and Korean (ko).
 
 Stay tuned as we continue to add support for more languages. If you have any language requests, please feel free to reach out.
 
@@ -116,15 +115,14 @@ model.load_checkpoint(config, checkpoint_dir="/path/to/xtts/", use_deepspeed=Tru
 model.cuda()
 
 print("Computing speaker latents...")
-gpt_cond_latent, diffusion_conditioning, speaker_embedding = model.get_conditioning_latents(audio_path=["reference.wav"])
+gpt_cond_latent, speaker_embedding = model.get_conditioning_latents(audio_path=["reference.wav"])
 
 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("xtts.wav", torch.tensor(out["wav"]).unsqueeze(0), 24000)
@@ -153,7 +151,7 @@ model.load_checkpoint(config, checkpoint_dir="/path/to/xtts/", use_deepspeed=Tru
 model.cuda()
 
 print("Computing speaker latents...")
-gpt_cond_latent, _, speaker_embedding = model.get_conditioning_latents(audio_path=["reference.wav"])
+gpt_cond_latent, speaker_embedding = model.get_conditioning_latents(audio_path=["reference.wav"])
 
 print("Inference...")
 t0 = time.time()
@@ -210,15 +208,14 @@ model.load_checkpoint(config, checkpoint_path=XTTS_CHECKPOINT, vocab_path=TOKENI
 model.cuda()
 
 print("Computing speaker latents...")
-gpt_cond_latent, diffusion_conditioning, speaker_embedding = model.get_conditioning_latents(audio_path=[SPEAKER_REFERENCE])
+gpt_cond_latent, 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)