612 lines
22 KiB
Python
612 lines
22 KiB
Python
# ported from: https://github.com/neonbjb/tortoise-tts
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import functools
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import math
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import random
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from transformers import GPT2Config
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from TTS.tts.layers.xtts.gpt_inference import GPT2InferenceModel
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from TTS.tts.layers.xtts.latent_encoder import ConditioningEncoder
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from TTS.tts.layers.xtts.perceiver_encoder import PerceiverResampler
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def null_position_embeddings(range, dim):
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return torch.zeros((range.shape[0], range.shape[1], dim), device=range.device)
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class LearnedPositionEmbeddings(nn.Module):
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def __init__(self, seq_len, model_dim, init=0.02, relative=False):
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super().__init__()
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# nn.Embedding
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self.emb = torch.nn.Embedding(seq_len, model_dim)
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# Initializing this way is standard for GPT-2
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self.emb.weight.data.normal_(mean=0.0, std=init)
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self.relative = relative
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self.seq_len = seq_len
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def forward(self, x):
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sl = x.shape[1]
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if self.relative:
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start = random.randint(sl, self.seq_len) - sl
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return self.emb(torch.arange(start, start + sl, device=x.device))
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else:
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return self.emb(torch.arange(0, sl, device=x.device))
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def get_fixed_embedding(self, ind, dev):
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return self.emb(torch.tensor([ind], device=dev)).unsqueeze(0)
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def build_hf_gpt_transformer(
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layers,
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model_dim,
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heads,
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max_mel_seq_len,
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max_text_seq_len,
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max_prompt_len,
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checkpointing,
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):
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"""
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GPT-2 implemented by the HuggingFace library.
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"""
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from transformers import GPT2Config, GPT2Model
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gpt_config = GPT2Config(
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vocab_size=256, # Unused.
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n_positions=max_mel_seq_len + max_text_seq_len + max_prompt_len,
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n_ctx=max_mel_seq_len + max_text_seq_len + max_prompt_len,
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n_embd=model_dim,
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n_layer=layers,
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n_head=heads,
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gradient_checkpointing=checkpointing,
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use_cache=not checkpointing,
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)
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gpt = GPT2Model(gpt_config)
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# Override the built in positional embeddings
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del gpt.wpe
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gpt.wpe = functools.partial(null_position_embeddings, dim=model_dim)
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# Built-in token embeddings are unused.
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del gpt.wte
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mel_pos_emb = (
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LearnedPositionEmbeddings(max_mel_seq_len, model_dim)
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if max_mel_seq_len != -1
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else functools.partial(null_position_embeddings, dim=model_dim)
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)
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text_pos_emb = (
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LearnedPositionEmbeddings(max_text_seq_len, model_dim)
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if max_mel_seq_len != -1
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else functools.partial(null_position_embeddings, dim=model_dim)
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)
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# gpt = torch.compile(gpt, mode="reduce-overhead", fullgraph=True)
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return gpt, mel_pos_emb, text_pos_emb, None, None
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class GPT(nn.Module):
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def __init__(
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self,
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start_text_token=261,
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stop_text_token=0,
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layers=8,
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model_dim=512,
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heads=8,
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max_text_tokens=120,
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max_mel_tokens=250,
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max_prompt_tokens=70,
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max_conditioning_inputs=1,
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code_stride_len=1024,
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number_text_tokens=256,
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num_audio_tokens=8194,
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start_audio_token=8192,
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stop_audio_token=8193,
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train_solo_embeddings=False,
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checkpointing=False,
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average_conditioning_embeddings=False,
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label_smoothing=0.0,
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use_perceiver_resampler=False,
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perceiver_cond_length_compression=256,
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):
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"""
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Args:
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"""
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super().__init__()
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self.label_smoothing = label_smoothing
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self.number_text_tokens = number_text_tokens
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self.start_text_token = start_text_token
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self.stop_text_token = stop_text_token
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self.num_audio_tokens = num_audio_tokens
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self.start_audio_token = start_audio_token
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self.stop_audio_token = stop_audio_token
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self.start_prompt_token = start_audio_token
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self.stop_prompt_token = stop_audio_token
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self.layers = layers
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self.heads = heads
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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.max_gen_mel_tokens = max_mel_tokens - self.max_conditioning_inputs - 2
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self.max_mel_tokens = -1 if max_mel_tokens == -1 else max_mel_tokens + 2 + self.max_conditioning_inputs
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self.max_text_tokens = -1 if max_text_tokens == -1 else max_text_tokens + 2
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self.max_prompt_tokens = max_prompt_tokens
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self.code_stride_len = code_stride_len
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self.conditioning_encoder = ConditioningEncoder(80, model_dim, num_attn_heads=heads)
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self.conditioning_dropout = nn.Dropout1d(0.1)
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self.average_conditioning_embeddings = average_conditioning_embeddings
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self.use_perceiver_resampler = use_perceiver_resampler
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self.perceiver_cond_length_compression = perceiver_cond_length_compression
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self.text_embedding = nn.Embedding(self.number_text_tokens, model_dim)
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self.mel_embedding = nn.Embedding(self.num_audio_tokens, model_dim)
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(
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self.gpt,
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self.mel_pos_embedding,
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self.text_pos_embedding,
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self.mel_layer_pos_embedding,
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self.text_layer_pos_embedding,
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) = build_hf_gpt_transformer(
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layers,
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model_dim,
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heads,
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self.max_mel_tokens,
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self.max_text_tokens,
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self.max_prompt_tokens,
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checkpointing,
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)
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if train_solo_embeddings:
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self.mel_solo_embedding = nn.Parameter(torch.randn(1, 1, model_dim) * 0.02, requires_grad=True)
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self.text_solo_embedding = nn.Parameter(torch.randn(1, 1, model_dim) * 0.02, requires_grad=True)
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else:
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self.mel_solo_embedding = 0
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self.text_solo_embedding = 0
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self.final_norm = nn.LayerNorm(model_dim)
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self.text_head = nn.Linear(model_dim, self.number_text_tokens)
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self.mel_head = nn.Linear(model_dim, self.num_audio_tokens)
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if self.use_perceiver_resampler:
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# XTTS v2
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self.conditioning_perceiver = PerceiverResampler(
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dim=model_dim,
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depth=2,
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dim_context=model_dim,
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num_latents=32,
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dim_head=64,
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heads=8,
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ff_mult=4,
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use_flash_attn=False,
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)
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else:
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# XTTS v1
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self.prompt_embedding = nn.Embedding(self.num_audio_tokens, model_dim)
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self.prompt_pos_embedding = LearnedPositionEmbeddings(24 * 9, model_dim)
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def get_grad_norm_parameter_groups(self):
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return {
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"conditioning_encoder": list(self.conditioning_encoder.parameters()),
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"conditioning_perceiver": list(self.conditioning_perceiver.parameters())
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if self.use_perceiver_resampler
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else None,
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"gpt": list(self.gpt.parameters()),
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"heads": list(self.text_head.parameters()) + list(self.mel_head.parameters()),
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}
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def init_gpt_for_inference(self, kv_cache=True, use_deepspeed=False):
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seq_length = self.max_prompt_tokens + self.max_mel_tokens + self.max_text_tokens + 1
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gpt_config = GPT2Config(
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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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n_embd=self.model_dim,
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n_layer=self.layers,
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n_head=self.heads,
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gradient_checkpointing=False,
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use_cache=True,
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)
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self.gpt_inference = GPT2InferenceModel(
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gpt_config,
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self.gpt,
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self.mel_pos_embedding,
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self.mel_embedding,
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self.final_norm,
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self.mel_head,
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kv_cache=kv_cache,
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)
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self.gpt.wte = self.mel_embedding
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if use_deepspeed:
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import deepspeed
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self.ds_engine = deepspeed.init_inference(
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model=self.gpt_inference.half(), # Transformers models
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mp_size=1, # Number of GPU
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dtype=torch.float32, # desired data type of output
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replace_method="auto", # Lets DS autmatically identify the layer to replace
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replace_with_kernel_inject=True, # replace the model with the kernel injector
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)
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self.gpt_inference = self.ds_engine.module.eval()
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def set_inputs_and_targets(self, input, start_token, stop_token):
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inp = F.pad(input, (1, 0), value=start_token)
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tar = F.pad(input, (0, 1), value=stop_token)
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return inp, tar
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def set_mel_padding(self, mel_input_tokens, code_lengths):
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"""
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Given mel tokens that are derived from a padded audio clip and the actual lengths of each batch element in
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that audio clip, reformats the tokens with stop_audio_token in place of the zero padding. This is required
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preformatting to create a working TTS model.
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"""
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# Set padding areas within MEL (currently it is coded with the MEL code for <zero>).
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for b in range(len(code_lengths)):
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actual_end = code_lengths[b]
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if actual_end < mel_input_tokens.shape[-1]:
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mel_input_tokens[b, actual_end:] = self.stop_audio_token
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return mel_input_tokens
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def get_logits(
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self,
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first_inputs,
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first_head,
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second_inputs=None,
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second_head=None,
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prompt=None,
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get_attns=False,
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return_latent=False,
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attn_mask_cond=None,
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attn_mask_text=None,
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attn_mask_mel=None,
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):
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if prompt is not None:
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offset = prompt.shape[1]
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if second_inputs is not None:
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emb = torch.cat([prompt, first_inputs, second_inputs], dim=1)
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else:
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emb = torch.cat([prompt, first_inputs], dim=1)
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# with torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=False, enable_mem_efficient=False):
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attn_mask = None
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if attn_mask_text is not None:
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attn_mask = torch.cat([attn_mask_text, attn_mask_mel], dim=1)
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if prompt is not None:
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attn_mask_cond = torch.ones(prompt.shape[0], offset, dtype=torch.bool, device=emb.device)
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attn_mask = torch.cat([attn_mask_cond, attn_mask], dim=1)
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gpt_out = self.gpt(
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inputs_embeds=emb,
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return_dict=True,
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output_attentions=get_attns,
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attention_mask=attn_mask,
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)
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if get_attns:
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return gpt_out.attentions
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enc = gpt_out.last_hidden_state[:, offset:]
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enc = self.final_norm(enc)
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if return_latent:
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return enc[:, : first_inputs.shape[1]], enc[:, -second_inputs.shape[1] :]
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first_logits = enc[:, : first_inputs.shape[1]]
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first_logits = first_head(first_logits)
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first_logits = first_logits.permute(0, 2, 1)
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if second_inputs is not None:
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second_logits = enc[:, -second_inputs.shape[1] :]
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second_logits = second_head(second_logits)
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second_logits = second_logits.permute(0, 2, 1)
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return first_logits, second_logits
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else:
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return first_logits
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def get_conditioning(self, speech_conditioning_input):
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speech_conditioning_input = (
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speech_conditioning_input.unsqueeze(1)
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if len(speech_conditioning_input.shape) == 3
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else speech_conditioning_input
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)
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conds = []
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for j in range(speech_conditioning_input.shape[1]):
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conds.append(self.conditioning_encoder(speech_conditioning_input[:, j]))
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conds = torch.stack(conds, dim=1)
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conds = conds.mean(dim=1)
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return conds
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def get_prompts(self, prompt_codes):
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"""
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Create a prompt from the mel codes. This is used to condition the model on the mel codes.
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Pad the prompt with start and stop mel tokens.
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"""
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prompt = prompt_codes
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if self.training:
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lengths = []
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# Compute the real prompt length based on the first encounter with the token 83 used for padding
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for i in range(prompt_codes.shape[0]):
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length = 0
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for j in range(prompt_codes.shape[1]):
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if prompt_codes[i, j] == 83:
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break
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else:
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length += 1
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lengths.append(length)
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# prompt_len = random.randint(1, 9) # in secs
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prompt_len = 3
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prompt_len = prompt_len * 24 # in frames
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if prompt_codes.shape[-1] >= prompt_len:
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for i in range(prompt_codes.shape[0]):
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if lengths[i] < prompt_len:
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start = 0
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else:
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start = random.randint(0, lengths[i] - prompt_len)
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prompt = prompt_codes[:, start : start + prompt_len]
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# add start and stop tokens
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prompt = F.pad(prompt, (1, 0), value=self.start_prompt_token)
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prompt = F.pad(prompt, (0, 1), value=self.stop_prompt_token)
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return prompt
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def get_style_emb(self, cond_input, return_latent=False):
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"""
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cond_input: (b, 80, s) or (b, 1, 80, s)
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conds: (b, 1024, s)
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"""
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conds = None
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if not return_latent:
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if cond_input.ndim == 4:
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cond_input = cond_input.squeeze(1)
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conds = self.conditioning_encoder(cond_input) # (b, d, s)
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if self.use_perceiver_resampler:
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conds = self.conditioning_perceiver(conds.permute(0, 2, 1)).transpose(1, 2) # (b, d, 32)
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else:
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# already computed
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conds = cond_input.unsqueeze(1)
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return conds
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def forward(
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self,
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text_inputs,
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text_lengths,
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audio_codes,
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wav_lengths,
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cond_mels=None,
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cond_idxs=None,
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cond_lens=None,
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cond_latents=None,
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return_attentions=False,
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return_latent=False,
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):
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"""
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Forward pass that uses both text and voice in either text conditioning mode or voice conditioning mode
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(actuated by `text_first`).
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text_inputs: long tensor, (b,t)
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text_lengths: long tensor, (b,)
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mel_inputs: long tensor, (b,m)
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wav_lengths: long tensor, (b,)
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cond_mels: MEL float tensor, (b, 1, 80,s)
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cond_idxs: cond start and end indexs, (b, 2)
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If return_attentions is specified, only logits are returned.
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If return_latent is specified, loss & logits are not computed or returned. Only the predicted latents are returned.
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"""
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# ❗ FIXIT
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if self.max_conditioning_inputs == 0:
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assert cond_mels is None, " ❗ cond_mels is not None, but max_conditioning_inputs == 0"
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max_text_len = text_lengths.max()
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code_lengths = torch.ceil(wav_lengths / self.code_stride_len).long() + 3
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if cond_lens is not None:
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if self.use_perceiver_resampler:
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cond_lens = cond_lens // self.perceiver_cond_length_compression
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else:
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cond_lens = cond_lens // self.code_stride_len
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if cond_idxs is not None:
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# recompute cond idxs for mel lengths
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for idx in range(cond_idxs.size(0)):
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if self.use_perceiver_resampler:
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cond_idxs[idx] = cond_idxs[idx] // self.perceiver_cond_length_compression
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else:
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cond_idxs[idx] = cond_idxs[idx] // self.code_stride_len
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# ensure that the cond_mel does not have padding
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# if cond_lens is not None and cond_idxs is None:
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# min_cond_len = torch.min(cond_lens)
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# cond_mels = cond_mels[:, :, :, :min_cond_len]
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# If len(codes) + 3 is larger than maxiumum allowed length, we truncate the codes.
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max_mel_len = code_lengths.max()
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if max_mel_len > audio_codes.shape[-1]:
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audio_codes = F.pad(audio_codes, (0, max_mel_len - audio_codes.shape[-1]))
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# 💖 Lovely assertions
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assert (
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max_mel_len <= audio_codes.shape[-1]
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), f" ❗ max_mel_len ({max_mel_len}) > audio_codes.shape[-1] ({audio_codes.shape[-1]})"
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assert (
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max_text_len <= text_inputs.shape[-1]
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), f" ❗ max_text_len ({max_text_len}) > text_inputs.shape[-1] ({text_inputs.shape[-1]})"
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# Append stop token to text inputs
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text_inputs = F.pad(text_inputs[:, :max_text_len], (0, 1), value=self.stop_text_token)
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# Append silence token to mel codes
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audio_codes = F.pad(audio_codes[:, :max_mel_len], (0, 1), value=self.stop_audio_token)
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# Pad mel codes with stop_audio_token
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audio_codes = self.set_mel_padding(
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audio_codes, code_lengths - 3
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) # -3 to get the real code lengths without consider start and stop tokens that was not added yet
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# Build input and target tensors
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# Prepend start token to inputs and append stop token to targets
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text_inputs, text_targets = self.set_inputs_and_targets(
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text_inputs, self.start_text_token, self.stop_text_token
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)
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audio_codes, mel_targets = self.set_inputs_and_targets(
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audio_codes, self.start_audio_token, self.stop_audio_token
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)
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# Set attn_mask
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attn_mask_cond = None
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attn_mask_text = None
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attn_mask_mel = None
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if not return_latent:
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attn_mask_cond = torch.ones(
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cond_mels.shape[0],
|
|
cond_mels.shape[-1],
|
|
dtype=torch.bool,
|
|
device=text_inputs.device,
|
|
)
|
|
attn_mask_text = torch.ones(
|
|
text_inputs.shape[0],
|
|
text_inputs.shape[1],
|
|
dtype=torch.bool,
|
|
device=text_inputs.device,
|
|
)
|
|
attn_mask_mel = torch.ones(
|
|
audio_codes.shape[0],
|
|
audio_codes.shape[1],
|
|
dtype=torch.bool,
|
|
device=audio_codes.device,
|
|
)
|
|
|
|
if cond_idxs is not None:
|
|
# use masking approach
|
|
for idx, r in enumerate(cond_idxs):
|
|
l = r[1] - r[0]
|
|
attn_mask_cond[idx, l:] = 0.0
|
|
elif cond_lens is not None:
|
|
for idx, l in enumerate(cond_lens):
|
|
attn_mask_cond[idx, l:] = 0.0
|
|
|
|
for idx, l in enumerate(text_lengths):
|
|
attn_mask_text[idx, l + 1 :] = 0.0
|
|
|
|
for idx, l in enumerate(code_lengths):
|
|
attn_mask_mel[idx, l + 1 :] = 0.0
|
|
|
|
# Compute text embeddings + positional embeddings
|
|
text_emb = self.text_embedding(text_inputs) + self.text_pos_embedding(text_inputs)
|
|
|
|
# Compute mel embeddings + positional embeddings
|
|
mel_emb = self.mel_embedding(audio_codes) + self.mel_pos_embedding(audio_codes)
|
|
|
|
# Compute speech conditioning input
|
|
if cond_latents is None:
|
|
cond_latents = self.get_style_emb(cond_mels).transpose(1, 2)
|
|
|
|
# Get logits
|
|
sub = -5 # don't ask me why 😄
|
|
if self.training:
|
|
sub = -1
|
|
|
|
text_logits, mel_logits = self.get_logits(
|
|
text_emb,
|
|
self.text_head,
|
|
mel_emb,
|
|
self.mel_head,
|
|
prompt=cond_latents,
|
|
get_attns=return_attentions,
|
|
return_latent=return_latent,
|
|
attn_mask_cond=attn_mask_cond,
|
|
attn_mask_text=attn_mask_text,
|
|
attn_mask_mel=attn_mask_mel,
|
|
)
|
|
if return_latent:
|
|
return mel_logits[:, :sub] # sub to prevent bla.
|
|
|
|
if return_attentions:
|
|
return mel_logits
|
|
|
|
# Set paddings to -1 to ignore them in loss
|
|
for idx, l in enumerate(text_lengths):
|
|
text_targets[idx, l + 1 :] = -1
|
|
|
|
for idx, l in enumerate(code_lengths):
|
|
mel_targets[idx, l + 1 :] = -1
|
|
|
|
# check if stoptoken is in every row of mel_targets
|
|
assert (mel_targets == self.stop_audio_token).sum() >= mel_targets.shape[
|
|
0
|
|
], f" ❗ mel_targets does not contain stop token ({self.stop_audio_token}) in every row."
|
|
|
|
# ignore the loss for the segment used for conditioning
|
|
# coin flip for the segment to be ignored
|
|
if cond_idxs is not None:
|
|
cond_start = cond_idxs[idx, 0]
|
|
cond_end = cond_idxs[idx, 1]
|
|
mel_targets[idx, cond_start:cond_end] = -1
|
|
|
|
# Compute losses
|
|
loss_text = F.cross_entropy(
|
|
text_logits, text_targets.long(), ignore_index=-1, label_smoothing=self.label_smoothing
|
|
)
|
|
loss_mel = F.cross_entropy(
|
|
mel_logits, mel_targets.long(), ignore_index=-1, label_smoothing=self.label_smoothing
|
|
)
|
|
return loss_text.mean(), loss_mel.mean(), mel_logits
|
|
|
|
def inference(self, cond_latents, text_inputs, **hf_generate_kwargs):
|
|
self.compute_embeddings(cond_latents, text_inputs)
|
|
return self.generate(cond_latents, text_inputs, **hf_generate_kwargs)
|
|
|
|
def compute_embeddings(
|
|
self,
|
|
cond_latents,
|
|
text_inputs,
|
|
):
|
|
text_inputs = F.pad(text_inputs, (0, 1), value=self.stop_text_token)
|
|
text_inputs = F.pad(text_inputs, (1, 0), value=self.start_text_token)
|
|
emb = self.text_embedding(text_inputs) + self.text_pos_embedding(text_inputs)
|
|
emb = torch.cat([cond_latents, emb], dim=1)
|
|
self.gpt_inference.store_prefix_emb(emb)
|
|
gpt_inputs = torch.full(
|
|
(
|
|
emb.shape[0],
|
|
emb.shape[1] + 1, # +1 for the start_audio_token
|
|
),
|
|
fill_value=1,
|
|
dtype=torch.long,
|
|
device=text_inputs.device,
|
|
)
|
|
gpt_inputs[:, -1] = self.start_audio_token
|
|
return gpt_inputs
|
|
|
|
def generate(
|
|
self,
|
|
cond_latents,
|
|
text_inputs,
|
|
**hf_generate_kwargs,
|
|
):
|
|
gpt_inputs = self.compute_embeddings(cond_latents, text_inputs)
|
|
gen = self.gpt_inference.generate(
|
|
gpt_inputs,
|
|
bos_token_id=self.start_audio_token,
|
|
pad_token_id=self.stop_audio_token,
|
|
eos_token_id=self.stop_audio_token,
|
|
max_length=self.max_gen_mel_tokens + gpt_inputs.shape[-1],
|
|
**hf_generate_kwargs,
|
|
)
|
|
if "return_dict_in_generate" in hf_generate_kwargs:
|
|
return gen.sequences[:, gpt_inputs.shape[1] :], gen
|
|
return gen[:, gpt_inputs.shape[1] :]
|
|
|
|
def get_generator(self, fake_inputs, **hf_generate_kwargs):
|
|
return self.gpt_inference.generate_stream(
|
|
fake_inputs,
|
|
bos_token_id=self.start_audio_token,
|
|
pad_token_id=self.stop_audio_token,
|
|
eos_token_id=self.stop_audio_token,
|
|
max_length=self.max_gen_mel_tokens + fake_inputs.shape[-1],
|
|
do_stream=True,
|
|
**hf_generate_kwargs,
|
|
)
|