#Heavily influenced by https://github.com/facebookresearch/audiocraft/blob/main/audiocraft/modules/conditioners.py import torch import logging, warnings import string import typing as tp import gc from .adp import NumberEmbedder from ..inference.utils import set_audio_channels from .factory import create_pretransform_from_config from .pretransforms import Pretransform from ..training.utils import copy_state_dict from .utils import load_ckpt_state_dict from torch import nn class Conditioner(nn.Module): def __init__( self, dim: int, output_dim: int, project_out: bool = False ): super().__init__() self.dim = dim self.output_dim = output_dim self.proj_out = nn.Linear(dim, output_dim) if (dim != output_dim or project_out) else nn.Identity() def forward(self, x: tp.Any) -> tp.Any: raise NotImplementedError() class IntConditioner(Conditioner): def __init__(self, output_dim: int, min_val: int=0, max_val: int=512 ): super().__init__(output_dim, output_dim) self.min_val = min_val self.max_val = max_val self.int_embedder = nn.Embedding(max_val - min_val + 1, output_dim).requires_grad_(True) def forward(self, ints: tp.List[int], device=None) -> tp.Any: #self.int_embedder.to(device) ints = torch.tensor(ints).to(device) ints = ints.clamp(self.min_val, self.max_val) int_embeds = self.int_embedder(ints).unsqueeze(1) return [int_embeds, torch.ones(int_embeds.shape[0], 1).to(device)] class NumberConditioner(Conditioner): ''' Conditioner that takes a list of floats, normalizes them for a given range, and returns a list of embeddings ''' def __init__(self, output_dim: int, min_val: float=0, max_val: float=1 ): super().__init__(output_dim, output_dim) self.min_val = min_val self.max_val = max_val self.embedder = NumberEmbedder(features=output_dim) def forward(self, floats: tp.List[float], device=None) -> tp.Any: # Cast the inputs to floats floats = [float(x) for x in floats] floats = torch.tensor(floats).to(device) floats = floats.clamp(self.min_val, self.max_val) normalized_floats = (floats - self.min_val) / (self.max_val - self.min_val) # Cast floats to same type as embedder embedder_dtype = next(self.embedder.parameters()).dtype normalized_floats = normalized_floats.to(embedder_dtype) float_embeds = self.embedder(normalized_floats).unsqueeze(1) return [float_embeds, torch.ones(float_embeds.shape[0], 1).to(device)] class CLAPTextConditioner(Conditioner): def __init__(self, output_dim: int, clap_ckpt_path, use_text_features = False, feature_layer_ix: int = -1, audio_model_type="HTSAT-base", enable_fusion=True, project_out: bool = False, finetune: bool = False): super().__init__(768 if use_text_features else 512, output_dim, project_out=project_out) self.use_text_features = use_text_features self.feature_layer_ix = feature_layer_ix self.finetune = finetune # Suppress logging from transformers previous_level = logging.root.manager.disable logging.disable(logging.ERROR) with warnings.catch_warnings(): warnings.simplefilter("ignore") try: import laion_clap from laion_clap.clap_module.factory import load_state_dict as clap_load_state_dict model = laion_clap.CLAP_Module(enable_fusion=enable_fusion, amodel=audio_model_type, device='cpu') if self.finetune: self.model = model else: self.__dict__["model"] = model state_dict = clap_load_state_dict(clap_ckpt_path) self.model.model.load_state_dict(state_dict, strict=False) if self.finetune: self.model.model.text_branch.requires_grad_(True) self.model.model.text_branch.train() else: self.model.model.text_branch.requires_grad_(False) self.model.model.text_branch.eval() finally: logging.disable(previous_level) del self.model.model.audio_branch gc.collect() torch.cuda.empty_cache() def get_clap_features(self, prompts, layer_ix=-2, device: tp.Any = "cuda"): prompt_tokens = self.model.tokenizer(prompts) attention_mask = prompt_tokens["attention_mask"].to(device=device, non_blocking=True) prompt_features = self.model.model.text_branch( input_ids=prompt_tokens["input_ids"].to(device=device, non_blocking=True), attention_mask=attention_mask, output_hidden_states=True )["hidden_states"][layer_ix] return prompt_features, attention_mask def forward(self, texts: tp.List[str], device: tp.Any = "cuda") -> tp.Any: self.model.to(device) if self.use_text_features: if len(texts) == 1: text_features, text_attention_mask = self.get_clap_features([texts[0], ""], layer_ix=self.feature_layer_ix, device=device) text_features = text_features[:1, ...] text_attention_mask = text_attention_mask[:1, ...] else: text_features, text_attention_mask = self.get_clap_features(texts, layer_ix=self.feature_layer_ix, device=device) return [self.proj_out(text_features), text_attention_mask] # Fix for CLAP bug when only one text is passed if len(texts) == 1: text_embedding = self.model.get_text_embedding([texts[0], ""], use_tensor=True)[:1, ...] else: text_embedding = self.model.get_text_embedding(texts, use_tensor=True) text_embedding = text_embedding.unsqueeze(1).to(device) return [self.proj_out(text_embedding), torch.ones(text_embedding.shape[0], 1).to(device)] class CLAPAudioConditioner(Conditioner): def __init__(self, output_dim: int, clap_ckpt_path, audio_model_type="HTSAT-base", enable_fusion=True, project_out: bool = False): super().__init__(512, output_dim, project_out=project_out) device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') # Suppress logging from transformers previous_level = logging.root.manager.disable logging.disable(logging.ERROR) with warnings.catch_warnings(): warnings.simplefilter("ignore") try: import laion_clap from laion_clap.clap_module.factory import load_state_dict as clap_load_state_dict model = laion_clap.CLAP_Module(enable_fusion=enable_fusion, amodel=audio_model_type, device='cpu') if self.finetune: self.model = model else: self.__dict__["model"] = model state_dict = clap_load_state_dict(clap_ckpt_path) self.model.model.load_state_dict(state_dict, strict=False) if self.finetune: self.model.model.audio_branch.requires_grad_(True) self.model.model.audio_branch.train() else: self.model.model.audio_branch.requires_grad_(False) self.model.model.audio_branch.eval() finally: logging.disable(previous_level) del self.model.model.text_branch gc.collect() torch.cuda.empty_cache() def forward(self, audios: tp.Union[torch.Tensor, tp.List[torch.Tensor], tp.Tuple[torch.Tensor]] , device: tp.Any = "cuda") -> tp.Any: self.model.to(device) if isinstance(audios, list) or isinstance(audios, tuple): audios = torch.cat(audios, dim=0) # Convert to mono mono_audios = audios.mean(dim=1) with torch.cuda.amp.autocast(enabled=False): audio_embedding = self.model.get_audio_embedding_from_data(mono_audios.float(), use_tensor=True) audio_embedding = audio_embedding.unsqueeze(1).to(device) return [self.proj_out(audio_embedding), torch.ones(audio_embedding.shape[0], 1).to(device)] class T5Conditioner(Conditioner): T5_MODELS = ["t5-small", "t5-base", "t5-large", "t5-3b", "t5-11b", "google/flan-t5-small", "google/flan-t5-base", "google/flan-t5-large", "google/flan-t5-xl", "google/flan-t5-xxl"] T5_MODEL_DIMS = { "t5-small": 512, "t5-base": 768, "t5-large": 1024, "t5-3b": 1024, "t5-11b": 1024, "t5-xl": 2048, "t5-xxl": 4096, "google/flan-t5-small": 512, "google/flan-t5-base": 768, "google/flan-t5-large": 1024, "google/flan-t5-3b": 1024, "google/flan-t5-11b": 1024, "google/flan-t5-xl": 2048, "google/flan-t5-xxl": 4096, } def __init__( self, output_dim: int, t5_model_name: str = "t5-base", max_length: str = 128, enable_grad: bool = False, project_out: bool = False ): assert t5_model_name in self.T5_MODELS, f"Unknown T5 model name: {t5_model_name}" super().__init__(self.T5_MODEL_DIMS[t5_model_name], output_dim, project_out=project_out) from transformers import T5EncoderModel, AutoTokenizer self.max_length = max_length self.enable_grad = enable_grad # Suppress logging from transformers previous_level = logging.root.manager.disable logging.disable(logging.ERROR) with warnings.catch_warnings(): warnings.simplefilter("ignore") try: # self.tokenizer = T5Tokenizer.from_pretrained(t5_model_name, model_max_length = max_length) # model = T5EncoderModel.from_pretrained(t5_model_name, max_length=max_length).train(enable_grad).requires_grad_(enable_grad) self.tokenizer = AutoTokenizer.from_pretrained(t5_model_name) model = T5EncoderModel.from_pretrained(t5_model_name).train(enable_grad).requires_grad_(enable_grad).to(torch.float16) finally: logging.disable(previous_level) if self.enable_grad: self.model = model else: self.__dict__["model"] = model def forward(self, texts: tp.List[str], device: tp.Union[torch.device, str]) -> tp.Tuple[torch.Tensor, torch.Tensor]: self.model.to(device) self.proj_out.to(device) encoded = self.tokenizer( texts, truncation=True, max_length=self.max_length, padding="max_length", return_tensors="pt", ) input_ids = encoded["input_ids"].to(device) attention_mask = encoded["attention_mask"].to(device).to(torch.bool) self.model.eval() with torch.cuda.amp.autocast(dtype=torch.float16) and torch.set_grad_enabled(self.enable_grad): embeddings = self.model( input_ids=input_ids, attention_mask=attention_mask )["last_hidden_state"] embeddings = self.proj_out(embeddings.float()) embeddings = embeddings * attention_mask.unsqueeze(-1).float() return embeddings, attention_mask class PhonemeConditioner(Conditioner): """ A conditioner that turns text into phonemes and embeds them using a lookup table Only works for English text Args: output_dim: the dimension of the output embeddings max_length: the maximum number of phonemes to embed project_out: whether to add another linear projection to the output embeddings """ def __init__( self, output_dim: int, max_length: int = 1024, project_out: bool = False, ): super().__init__(output_dim, output_dim, project_out=project_out) from g2p_en import G2p self.max_length = max_length self.g2p = G2p() # Reserving 0 for padding, 1 for ignored self.phoneme_embedder = nn.Embedding(len(self.g2p.phonemes) + 2, output_dim) def forward(self, texts: tp.List[str], device: tp.Union[torch.device, str]) -> tp.Tuple[torch.Tensor, torch.Tensor]: self.phoneme_embedder.to(device) self.proj_out.to(device) batch_phonemes = [self.g2p(text) for text in texts] # shape [batch_size, length] phoneme_ignore = [" ", *string.punctuation] # Remove ignored phonemes and cut to max length batch_phonemes = [[p if p not in phoneme_ignore else "_" for p in phonemes] for phonemes in batch_phonemes] # Convert to ids phoneme_ids = [[self.g2p.p2idx[p] + 2 if p in self.g2p.p2idx else 1 for p in phonemes] for phonemes in batch_phonemes] #Pad to match longest and make a mask tensor for the padding longest = max([len(ids) for ids in phoneme_ids]) phoneme_ids = [ids + [0] * (longest - len(ids)) for ids in phoneme_ids] phoneme_ids = torch.tensor(phoneme_ids).to(device) # Convert to embeddings phoneme_embeds = self.phoneme_embedder(phoneme_ids) phoneme_embeds = self.proj_out(phoneme_embeds) return phoneme_embeds, torch.ones(phoneme_embeds.shape[0], phoneme_embeds.shape[1]).to(device) class TokenizerLUTConditioner(Conditioner): """ A conditioner that embeds text using a lookup table on a pretrained tokenizer's vocabulary Args: tokenizer_name: the name of the tokenizer from the Hugging Face transformers library output_dim: the dimension of the output embeddings max_length: the maximum length of the text to embed project_out: whether to add another linear projection to the output embeddings """ def __init__( self, tokenizer_name: str, # Name of a tokenizer from the Hugging Face transformers library output_dim: int, max_length: int = 1024, project_out: bool = False, ): super().__init__(output_dim, output_dim, project_out=project_out) from transformers import AutoTokenizer # Suppress logging from transformers previous_level = logging.root.manager.disable logging.disable(logging.ERROR) with warnings.catch_warnings(): warnings.simplefilter("ignore") try: self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_name) finally: logging.disable(previous_level) self.max_length = max_length self.token_embedder = nn.Embedding(len(self.tokenizer), output_dim) def forward(self, texts: tp.List[str], device: tp.Union[torch.device, str]) -> tp.Tuple[torch.Tensor, torch.Tensor]: self.proj_out.to(device) encoded = self.tokenizer( texts, truncation=True, max_length=self.max_length, padding="max_length", return_tensors="pt", ) input_ids = encoded["input_ids"].to(device) attention_mask = encoded["attention_mask"].to(device).to(torch.bool) embeddings = self.token_embedder(input_ids) embeddings = self.proj_out(embeddings) embeddings = embeddings * attention_mask.unsqueeze(-1).float() return embeddings, attention_mask class PretransformConditioner(Conditioner): """ A conditioner that uses a pretransform's encoder for conditioning Args: pretransform: an instantiated pretransform to use for conditioning output_dim: the dimension of the output embeddings """ def __init__(self, pretransform: Pretransform, output_dim: int): super().__init__(pretransform.encoded_channels, output_dim) self.pretransform = pretransform def forward(self, audio: tp.Union[torch.Tensor, tp.List[torch.Tensor], tp.Tuple[torch.Tensor]], device: tp.Union[torch.device, str]) -> tp.Tuple[torch.Tensor, torch.Tensor]: self.pretransform.to(device) self.proj_out.to(device) if isinstance(audio, list) or isinstance(audio, tuple): audio = torch.cat(audio, dim=0) # Convert audio to pretransform input channels audio = set_audio_channels(audio, self.pretransform.io_channels) latents = self.pretransform.encode(audio) latents = self.proj_out(latents) return [latents, torch.ones(latents.shape[0], latents.shape[2]).to(latents.device)] class MultiConditioner(nn.Module): """ A module that applies multiple conditioners to an input dictionary based on the keys Args: conditioners: a dictionary of conditioners with keys corresponding to the keys of the conditioning input dictionary (e.g. "prompt") default_keys: a dictionary of default keys to use if the key is not in the input dictionary (e.g. {"prompt_t5": "prompt"}) """ def __init__(self, conditioners: tp.Dict[str, Conditioner], default_keys: tp.Dict[str, str] = {}): super().__init__() self.conditioners = nn.ModuleDict(conditioners) self.default_keys = default_keys def forward(self, batch_metadata: tp.List[tp.Dict[str, tp.Any]], device: tp.Union[torch.device, str]) -> tp.Dict[str, tp.Any]: output = {} for key, conditioner in self.conditioners.items(): condition_key = key conditioner_inputs = [] for x in batch_metadata: if condition_key not in x: if condition_key in self.default_keys: condition_key = self.default_keys[condition_key] else: raise ValueError(f"Conditioner key {condition_key} not found in batch metadata") #Unwrap the condition info if it's a single-element list or tuple, this is to support collation functions that wrap everything in a list if isinstance(x[condition_key], list) or isinstance(x[condition_key], tuple) and len(x[condition_key]) == 1: conditioner_input = x[condition_key][0] else: conditioner_input = x[condition_key] conditioner_inputs.append(conditioner_input) output[key] = conditioner(conditioner_inputs, device) return output def create_multi_conditioner_from_conditioning_config(config: tp.Dict[str, tp.Any]) -> MultiConditioner: """ Create a MultiConditioner from a conditioning config dictionary Args: config: the conditioning config dictionary device: the device to put the conditioners on """ conditioners = {} cond_dim = config["cond_dim"] default_keys = config.get("default_keys", {}) for conditioner_info in config["configs"]: id = conditioner_info["id"] conditioner_type = conditioner_info["type"] conditioner_config = {"output_dim": cond_dim} conditioner_config.update(conditioner_info["config"]) if conditioner_type == "t5": conditioners[id] = T5Conditioner(**conditioner_config) elif conditioner_type == "clap_text": conditioners[id] = CLAPTextConditioner(**conditioner_config) elif conditioner_type == "clap_audio": conditioners[id] = CLAPAudioConditioner(**conditioner_config) elif conditioner_type == "int": conditioners[id] = IntConditioner(**conditioner_config) elif conditioner_type == "number": conditioners[id] = NumberConditioner(**conditioner_config) elif conditioner_type == "phoneme": conditioners[id] = PhonemeConditioner(**conditioner_config) elif conditioner_type == "lut": conditioners[id] = TokenizerLUTConditioner(**conditioner_config) elif conditioner_type == "pretransform": sample_rate = conditioner_config.pop("sample_rate", None) assert sample_rate is not None, "Sample rate must be specified for pretransform conditioners" pretransform = create_pretransform_from_config(conditioner_config.pop("pretransform_config"), sample_rate=sample_rate) if conditioner_config.get("pretransform_ckpt_path", None) is not None: pretransform.load_state_dict(load_ckpt_state_dict(conditioner_config.pop("pretransform_ckpt_path"))) conditioners[id] = PretransformConditioner(pretransform, **conditioner_config) else: raise ValueError(f"Unknown conditioner type: {conditioner_type}") return MultiConditioner(conditioners, default_keys=default_keys)