added compat for coqui's XTTS
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@ -338,7 +338,7 @@ class TextToSpeech:
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self.loading = False
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def load_autoregressive_model(self, autoregressive_model_path):
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def load_autoregressive_model(self, autoregressive_model_path, is_xtts=False):
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if hasattr(self,"autoregressive_model_path") and os.path.samefile(self.autoregressive_model_path, autoregressive_model_path):
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return
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@ -356,10 +356,38 @@ class TextToSpeech:
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if hasattr(self, 'autoregressive'):
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del self.autoregressive
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self.autoregressive = UnifiedVoice(max_mel_tokens=604, max_text_tokens=402, max_conditioning_inputs=2, layers=30,
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model_dim=1024,
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heads=16, number_text_tokens=255, start_text_token=255, checkpointing=False,
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train_solo_embeddings=False).cpu().eval()
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# XTTS requires a different "dimensionality" for its autoregressive model
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if new_hash == "e4ce21eae0043f7691d6a6c8540b74b8" or is_xtts:
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dimensionality = {
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"max_mel_tokens": 605,
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"max_text_tokens": 402,
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"max_prompt_tokens": 70,
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"max_conditioning_inputs": 1,
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"layers": 30,
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"model_dim": 1024,
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"heads": 16,
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"number_text_tokens": 5023, # -1
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"start_text_token": 261,
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"stop_text_token": 0,
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"number_mel_codes": 8194,
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"start_mel_token": 8192,
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"stop_mel_token": 8193,
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}
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else:
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dimensionality = {
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"max_mel_tokens": 604,
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"max_text_tokens": 402,
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"max_conditioning_inputs": 2,
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"layers": 30,
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"model_dim": 1024,
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"heads": 16,
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"number_text_tokens": 255,
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"start_text_token": 255,
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"checkpointing": False,
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"train_solo_embeddings": False
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}
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self.autoregressive = UnifiedVoice(**dimensionality).cpu().eval()
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self.autoregressive.load_state_dict(torch.load(self.autoregressive_model_path))
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self.autoregressive.post_init_gpt2_config(use_deepspeed=self.use_deepspeed, kv_cache=self.use_kv_cache)
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if self.preloaded_tensors:
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@ -380,9 +408,21 @@ class TextToSpeech:
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if hasattr(self, 'diffusion'):
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del self.diffusion
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self.diffusion = DiffusionTts(model_channels=1024, num_layers=10, in_channels=100, out_channels=200,
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in_latent_channels=1024, in_tokens=8193, dropout=0, use_fp16=False, num_heads=16,
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layer_drop=0, unconditioned_percentage=0).cpu().eval()
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# XTTS does not require a different "dimensionality" for its diffusion model
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dimensionality = {
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"model_channels": 1024,
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"num_layers": 10,
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"in_channels": 100,
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"out_channels": 200,
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"in_latent_channels": 1024,
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"in_tokens": 8193,
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"dropout": 0,
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"use_fp16": False,
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"num_heads": 16,
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"layer_drop": 0,
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"unconditioned_percentage": 0
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}
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self.diffusion = DiffusionTts(**dimensionality)
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self.diffusion.load_state_dict(torch.load(get_model_path('diffusion_decoder.pth', self.models_dir)))
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if self.preloaded_tensors:
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self.diffusion = migrate_to_device( self.diffusion, self.device )
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@ -283,9 +283,9 @@ class MelEncoder(nn.Module):
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class UnifiedVoice(nn.Module):
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def __init__(self, layers=8, model_dim=512, heads=8, max_text_tokens=120, max_mel_tokens=250, max_conditioning_inputs=1,
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def __init__(self, layers=8, model_dim=512, heads=8, max_text_tokens=120, max_prompt_tokens=2, max_mel_tokens=250, max_conditioning_inputs=1,
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mel_length_compression=1024, number_text_tokens=256,
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start_text_token=None, number_mel_codes=8194, start_mel_token=8192,
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start_text_token=None, stop_text_token=0, number_mel_codes=8194, start_mel_token=8192,
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stop_mel_token=8193, train_solo_embeddings=False, use_mel_codes_as_input=True,
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checkpointing=True, types=1):
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"""
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@ -295,6 +295,7 @@ class UnifiedVoice(nn.Module):
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heads: Number of transformer heads. Must be divisible by model_dim. Recommend model_dim//64
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max_text_tokens: Maximum number of text tokens that will be encountered by model.
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max_mel_tokens: Maximum number of MEL tokens that will be encountered by model.
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max_prompt_tokens: compat set to 2, 70 for XTTS
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max_conditioning_inputs: Maximum number of conditioning inputs provided to the model. If (1), conditioning input can be of format (b,80,s), otherwise (b,n,80,s).
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mel_length_compression: The factor between <number_input_samples> and <mel_tokens>. Used to compute MEL code padding given wav input length.
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number_text_tokens:
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@ -311,7 +312,7 @@ class UnifiedVoice(nn.Module):
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self.number_text_tokens = number_text_tokens
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self.start_text_token = number_text_tokens * types if start_text_token is None else start_text_token
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self.stop_text_token = 0
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self.stop_text_token = stop_text_token
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self.number_mel_codes = number_mel_codes
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self.start_mel_token = start_mel_token
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self.stop_mel_token = stop_mel_token
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@ -319,6 +320,7 @@ class UnifiedVoice(nn.Module):
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self.heads = heads
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self.max_mel_tokens = max_mel_tokens
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self.max_text_tokens = max_text_tokens
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self.max_prompt_tokens = max_prompt_tokens
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self.model_dim = model_dim
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self.max_conditioning_inputs = max_conditioning_inputs
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self.mel_length_compression = mel_length_compression
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@ -353,7 +355,7 @@ class UnifiedVoice(nn.Module):
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module.weight.data.normal_(mean=0.0, std=.02)
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def post_init_gpt2_config(self, use_deepspeed=False, kv_cache=False):
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seq_length = self.max_mel_tokens + self.max_text_tokens + 2
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seq_length = self.max_mel_tokens + self.max_text_tokens + self.max_prompt_tokens
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gpt_config = GPT2Config(vocab_size=self.max_mel_tokens,
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n_positions=seq_length,
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n_ctx=seq_length,
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@ -373,7 +375,7 @@ class UnifiedVoice(nn.Module):
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self.inference_model = self.ds_engine.module.eval()
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else:
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self.inference_model = self.inference_model.eval()
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self.gpt.wte = self.mel_embedding
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def build_aligned_inputs_and_targets(self, input, start_token, stop_token):
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@ -494,7 +496,7 @@ class UnifiedVoice(nn.Module):
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def inference_speech(self, speech_conditioning_latent, text_inputs, input_tokens=None, num_return_sequences=1,
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max_generate_length=None, typical_sampling=False, typical_mass=.9, **hf_generate_kwargs):
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seq_length = self.max_mel_tokens + self.max_text_tokens + 2
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seq_length = self.max_mel_tokens + self.max_text_tokens + self.max_prompt_tokens
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if not hasattr(self, 'inference_model'):
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self.post_init_gpt2_config(kv_cache=self.kv_cache)
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