762 lines
25 KiB
Python
762 lines
25 KiB
Python
"""
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# an AR + NAR model that handles:
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* inferencing the primary RVQ level in an autoregressive manner (AR)
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* inferencing the remaining RVQ levels in parallel (NAR)
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This model can fully handle being trained as a unified model (AR + NAR) or separate models (AR | NAR).
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It's recommended to train as a unified model, then "distill" knowledge of each tasks separately, just in case.
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"""
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from .base import Base, list_to_tensor, Categorical
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from ..config import cfg
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import torch
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from torch.nn.utils.rnn import pad_sequence
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import random
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import math
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import time
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from einops import rearrange
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from torch import Tensor
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from tqdm import trange
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import logging
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_logger = logging.getLogger(__name__)
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from ..emb.qnt import trim, encode_as_embedding, get_silence
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from ..utils import get_devices, setup_logging, timer
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from .lora import enable_lora
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def clamp(n, lo, hi):
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return max(lo, min(n, hi))
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class AR_NAR(Base):
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def forward(
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self,
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text_list: list[Tensor],
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proms_list: list[Tensor],
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resps_list: list[Tensor] | None = None,
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task_list: list[Tensor] | None = None,
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lang_list: list[Tensor] | None = None,
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tone_list: list[Tensor] | None = None,
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len_list: list[Tensor] | None = None,
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training: bool | int | None = None,
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max_steps: int = 1000,
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max_levels: int = 0,
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input_prompt_prefix: bool = False,
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prefix_silence: float = 1.0,
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sampling_temperature: float = 1.0,
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sampling_min_temperature: float = -1.0,
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sampling_top_k: int = -100,
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sampling_top_p: float = 1.0,
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sampling_min_p: float = 0.0,
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sampling_repetition_penalty: float = 1.0,
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sampling_repetition_penalty_decay: float = 0.0,
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sampling_length_penalty: float = 0.0,
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sampling_beam_width: int = 0,
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sampling_mirostat_tau: float = 0.0,
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sampling_mirostat_eta: float = 0.1,
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sampling_dry_multiplier=0.0,
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sampling_dry_base=1.75,
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sampling_dry_allowed_length=2,
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sampling_entropix=False,
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sampling_layer_skip: bool = False,
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sampling_layer_skip_exit_layer: int = -1,
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sampling_layer_skip_entropy_threshold: float = -1,
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sampling_layer_skip_varentropy_threshold: float = -1,
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sampling_refine_on_stop: bool = False,
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disable_tqdm=False,
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use_lora=None,
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):
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text_task = [ "stt" ]
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if text_list is not None:
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default_task = "tts"
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device = text_list[0].device
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batch_size = len(text_list)
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else:
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default_task = "stt"
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device = resps_list[0].device
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batch_size = len(resps_list)
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# generate task list if not provided
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if task_list is None:
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task_list = [ default_task for _ in range(batch_size) ]
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has_none = resps_list is None or text_list is None
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if not has_none:
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for i, task in enumerate( task_list ):
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if resps_list[i] is None or text_list[i] is None:
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has_none = True
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break
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# is training or NAR
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if not has_none:
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n_levels_set = {r.shape[-1] for r in resps_list}
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n_levels = next(iter(n_levels_set))
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# implicit
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if training is None:
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training = 0 if n_levels == self.n_resp_levels else None
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# is training
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if training is not None:
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# specifies how to sample probabilities of which RVQ levels to train against
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rvq_levels_p = self.config.experimental.rvq_levels_p if self.config is not None else "equal"
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# determines which RVQ level to target per batch
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quant_level_range = self.config.experimental.rvq_level_range if self.config is not None and self.config.experimental.rvq_level_range else [ 0 if self.causal else 1, self.n_resp_levels - 1 ]
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# rate to perform token dropout errors
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token_dropout_error = self.config.experimental.token_dropout_error
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# RVQ levels to apply token dropout on
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token_dropout_rvq_levels = self.config.experimental.token_dropout_rvq_levels
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# implicitly set it to all levels
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if not token_dropout_rvq_levels:
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token_dropout_rvq_levels = [0, self.resp_levels - 1]
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# allow passing a specific distribution of RVQ levels
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rvq_levels_p = rvq_levels_p if isinstance(rvq_levels_p, list) else []
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if not rvq_levels_p:
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lo, hi = quant_level_range[0], quant_level_range[1] + 1
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# randomly select a target RVQ-bin level (0 being AR, 1+ being NAR)
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if rvq_levels_p == "equal":
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rvq_levels_p = [ i for i in range( lo, hi ) ]
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else:
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# yuck
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rvq_levels_p = sum([[i for _ in range(hi - i)] for i in range( lo, hi ) ], [])
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# input RVQ levels
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quant_levels = [ random.choice( rvq_levels_p ) for i in range(batch_size) ]
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for i, task in enumerate( task_list ):
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if task in text_task:
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quant_levels[i] = 0 # self.n_resp_levels - 1
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# trim resps to only contain all levels below the target level
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resps_list = [r if t in text_task else r[..., :l+1] for r, l, t in zip(resps_list, quant_levels, task_list)]
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# tensor to cat for RVQ level 0
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text_stop_sequence = torch.tensor([[2] * 1], device=device, dtype=torch.int16)
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audio_stop_sequence = torch.tensor([[self.stop_token] * 1], device=device, dtype=torch.int16)
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# I hate python's value/reference semantics so much
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for i, quant_level, resps, proms, task in zip(range(batch_size), quant_levels, resps_list, proms_list, task_list):
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# cap quant_level if it exceeds its corresponding resp/prom
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if quant_level >= resps.shape[-1]:
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quant_levels[i] = resps.shape[-1] - 1
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# proms could be a Tensor, list[Tensor], or None
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if isinstance( proms, torch.Tensor ):
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if quant_level >= proms.shape[-1]:
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quant_levels[i] = proms.shape[-1] - 1
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elif isinstance( proms, list ):
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for j, prom in enumerate( proms ):
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if not isinstance( prom, torch.Tensor ):
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continue
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if quant_level >= prom.shape[-1]:
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quant_levels[i] = prom.shape[-1] - 1
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# apply token dropout error compensation
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if token_dropout_error > 0 and (token_dropout_rvq_levels[0] <= quant_level and quant_level <= token_dropout_rvq_levels[1]):
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steps = resps.shape[0]
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for l in range( quant_level ):
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for t in range( steps ):
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token = resps[t, l].item()
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if random.random() < token_dropout_error:
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offset = 1 * ( 1 if random.random() < 0.5 else -1 )
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resps_list[i][t, l] = clamp(token + offset, 1, 1022) # +- 1
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# only apply stop token for RVQ level 0
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if quant_level <= 0:
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# append stop tokens for AR
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if task in text_task:
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#text_list[i] = torch.cat([ resps, text_stop_sequence ])
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...
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else:
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resps_list[i] = torch.cat([ resps, audio_stop_sequence ])
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inputs = self.inputs(
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text_list=text_list,
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proms_list=proms_list,
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resps_list=resps_list,
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lang_list=lang_list,
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tone_list=tone_list,
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task_list=task_list,
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quant_levels=quant_levels,
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)
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return super().forward(
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inputs=inputs,
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quant_levels=quant_levels, # could technically just grab this from the above inputs since they're included as an RVQ level token
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)
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# is NAR
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if max_levels == 0:
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max_levels = self.n_max_levels - 1
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# expand if given a raw 1D tensor
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for i, resp in enumerate(resps_list):
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if resp.dim() == 1:
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resps_list[i] = resp.unsqueeze(-1)
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prev_list = resps_list
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sampling_layer_skip_variables = {} if sampling_layer_skip else None
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if sampling_layer_skip:
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if sampling_layer_skip_entropy_threshold >= 0:
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sampling_layer_skip_variables["entropy_threshold"] = sampling_layer_skip_entropy_threshold
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if sampling_layer_skip_varentropy_threshold >= 0:
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sampling_layer_skip_variables["varentropy_threshold"] = sampling_layer_skip_varentropy_threshold
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if sampling_layer_skip_exit_layer >= 0:
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sampling_layer_skip_variables["max_layer"] = sampling_layer_skip_exit_layer
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for n in trange( max_levels, desc="NAR", disable=disable_tqdm ):
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level = prev_list[0].shape[-1]
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if level >= max_levels + 1: # min(max_levels + 1, self.n_resp_levels): # commented out to experiment with exceeding trained levels
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break
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if cfg.lora is not None:
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enable_lora( self, cfg.lora.active_level( level ) if use_lora is None else use_lora )
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quant_levels = [ level for _ in range(batch_size) ] # torch.full((len(text_list),), level)
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inputs = self.inputs(
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text_list=text_list,
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proms_list=proms_list,
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resps_list=prev_list,
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lang_list=lang_list,
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tone_list=tone_list,
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quant_levels=quant_levels,
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)
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output = super().forward(
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inputs=inputs,
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quant_levels=quant_levels,
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layer_skip_variables=sampling_layer_skip_variables,
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)
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logits, state = output.logits, output.state
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sampled = super().sample(
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logits=logits,
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prev_list=prev_list,
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quant_levels=quant_levels,
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temperature=sampling_temperature,
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#min_temperature=sampling_min_temperature,
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#top_p=sampling_top_p,
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#top_k=sampling_top_k,
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#min_p=sampling_min_p,
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#repetition_penalty=sampling_repetition_penalty,
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#repetition_penalty_decay=sampling_repetition_penalty_decay,
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#length_penalty=sampling_length_penalty,
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#beam_width=sampling_beam_width,
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#mirostat=mirostat,
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)
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resps_list = sampled[0]
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prev_list = [ torch.cat([rs, r.unsqueeze(-1).to(device=device)], dim=-1) for rs, r in zip(prev_list, resps_list) ]
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return prev_list
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# is AR
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if cfg.lora is not None:
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enable_lora( self, cfg.lora.active_level( 0 ) if use_lora is None else use_lora )
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# STT
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start_slice = [ 0 for _ in range(batch_size) ]
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sequence_list = [ torch.zeros(0, device=device).to(torch.int16) for _ in range(batch_size) ]
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stopped = torch.zeros(batch_size, device=device).bool()
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audio_stop_token = self.stop_token
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text_stop_token = 2
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state = None
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mirostat = [
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{"n": 1024, "tau": sampling_mirostat_tau, "eta": sampling_mirostat_eta, "max_surprise": sampling_mirostat_eta * 2, "error_surprise": 0, "running_total_surprise": 0}
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] * batch_size if sampling_mirostat_tau > 0.0 else None
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scores = [ 1.0 ] * sampling_beam_width
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metrics = []
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# ick
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"""
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low_temperature = False # sampling_temperature < 0.6 # sampling_repetition_penalty == 1.0 and sampling_temperature == 0.0 #
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low_temperature_range = cfg.dataset.frames_per_second * 5
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original_sampling_temperature = sampling_temperature
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original_sampling_repetition_penalty = sampling_repetition_penalty
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original_sampling_repetition_penalty_decay = sampling_repetition_penalty_decay
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"""
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sampling_layer_skip_variables = {} if sampling_layer_skip else None
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if sampling_layer_skip:
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if sampling_layer_skip_entropy_threshold >= 0:
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sampling_layer_skip_variables["entropy_threshold"] = sampling_layer_skip_entropy_threshold
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if sampling_layer_skip_varentropy_threshold >= 0:
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sampling_layer_skip_variables["varentropy_threshold"] = sampling_layer_skip_varentropy_threshold
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if sampling_layer_skip_exit_layer >= 0:
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sampling_layer_skip_variables["max_layer"] = sampling_layer_skip_exit_layer
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for i, sequence in enumerate( sequence_list ):
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# add <bos> to text for STT
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if task_list[i] in text_task:
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start_slice[i] = 1
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sequence_list[i] = torch.cat([sequence_list[i], torch.tensor([1], dtype=torch.int16, device=device)])
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# treat input prompt as initial resp (by prefixing with the prompt instead)
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elif input_prompt_prefix:
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start_slice[i] = proms_list[i].shape[0]
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sequence_list[i], proms_list[i] = proms_list[i][:, 0], sequence_list[i]
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elif prefix_silence > 0:
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sequence_list[i] = get_silence(prefix_silence, device=sequence_list[i].device)
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sequence_list[i] = sequence_list[i][:, 0]
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# start_slice[i] = sequence_list[i].shape[0]
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# get next in sequence
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for n in trange(max_steps // max(1, self.causal_size), desc="AR", disable=disable_tqdm):
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# it would technically be faster to just append the new token's embedding to the inputs, but there's a VERY small performance gain from doing it, so it's not worth it
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text_list = [ sequence_list[i] if task in text_task else text_list[i] for i, task in enumerate(task_list) ]
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resps_list = [ sequence_list[i] if task not in text_task else resps_list[i] for i, task in enumerate(task_list) ]
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# greedy sampling in the AR *does* work, but requires some quasi-exotic sampling to work around the initial burst of garbage from polluting the rest of the sequence
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# naturally, rep pen wrangles this initial burst of noise, but naively relying on rep_pen is no good, as it fails after ~6 seconds of audio
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# however, switching to a default sampling temperature with "clean greedy sampled codes" will make the rest of sequence sound as if it were greedy sampled
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# to-do: tune these values, maybe have it factor based on confidence scores or something
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"""
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if low_temperature:
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enabled = n < low_temperature_range
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sampling_repetition_penalty = 1.125 if enabled else 1.25
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#sampling_repetition_penalty_decay = 0.0 if enabled else original_sampling_repetition_penalty_decay
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#sampling_temperature = original_sampling_temperature if enabled else 1.0
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"""
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inputs = self.inputs(
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text_list=text_list,
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proms_list=proms_list,
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resps_list=resps_list,
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lang_list=lang_list,
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tone_list=tone_list,
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len_list=len_list,
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task_list=task_list,
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quant_levels=[ 0 for _ in range( max( batch_size, sampling_beam_width ) ) ]
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)
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# to-do: find an elegant way to write this
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output = super().forward(
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inputs=inputs,
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state=state,
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layer_skip_variables=sampling_layer_skip_variables,
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output_attentions=sampling_entropix,
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)
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logits, state = output.logits, output.state
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sampled = super().sample(
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logits=logits,
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prev_list=None if sampling_repetition_penalty == 1.0 and sampling_length_penalty == 0.0 else [ resps_list[i] if task not in text_task else text_list[i] for i, task in enumerate( task_list ) ],
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temperature=sampling_temperature,
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min_temperature=sampling_min_temperature,
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top_p=sampling_top_p,
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top_k=sampling_top_k,
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min_p=sampling_min_p,
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repetition_penalty=sampling_repetition_penalty,
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repetition_penalty_decay=sampling_repetition_penalty_decay,
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length_penalty=sampling_length_penalty,
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beam_width=sampling_beam_width,
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mirostat=mirostat,
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dry_multiplier=sampling_dry_multiplier,
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dry_base=sampling_dry_base,
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dry_allowed_length=sampling_dry_allowed_length,
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attentions=output.attentions if sampling_entropix else None,
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)
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r = sampled[0]
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if cfg.experimental:
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if sampled.entropy:
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metrics.append( sampled.entropy )
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elif sampled.scores:
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metrics.append( [ { "p": p[0], "exited_layer": output.exited_layer } for p in sampled.scores ] )
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if mirostat is not None:
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mirostat = sampled.scores
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elif sampling_beam_width > 0:
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# expand tuple
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s = sampled.scores
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# first step, expand batch
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if batch_size == 1:
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batch_size = sampling_beam_width
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text_list = text_list * sampling_beam_width
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proms_list = proms_list * sampling_beam_width
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sequence_list = sequence_list * sampling_beam_width
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task_list = task_list * sampling_beam_width
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start_slice = start_slice * sampling_beam_width
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stopped = torch.zeros(batch_size, device=device).bool()
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scores = [ scores[i] + score for i, score in enumerate(s) ]
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# append tokens
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for i, ri in enumerate(r):
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task = task_list[i]
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stop_token = audio_stop_token if task not in text_task else text_stop_token
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if stop_token in ri:
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stopped[i] = True
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sequence_list[i] = torch.cat([sequence_list[i], ri.to(device)])
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# stop token found
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# stopped |= r == stop_token
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if stopped.all().item():
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break
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# to-do for layerskip / speculative sampling: rerun the last sequence again at max depth
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if metrics:
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from ..plot import plot_sample_metrics
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filename = "metrics"
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if sampling_entropix:
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filename += f'[entropix]'
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if sampling_layer_skip_exit_layer >= 0:
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filename += f'[{sampling_layer_skip_exit_layer+1}]'
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plot_sample_metrics( metrics, filename=f'{filename}.png' )
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# pick the best scoring candidate
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# desu this is always going to be candidate 0
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if sampling_beam_width:
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sequence_list = sequence_list[:1]
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task_list = task_list[:1]
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# remove stop token
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sequence_list = [self._prune(r, audio_stop_token if task_list[i] not in text_task else text_stop_token) for i, r in enumerate(sequence_list)]
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# remove <bos>
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sequence_list = [ sequence_list[i][start_slice[i]:] for i, task in enumerate( task_list ) ]
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if sampling_refine_on_stop:
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# get how much we need to slice from the end
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slice_lengths = [ sequence.shape[-1] for sequence in sequence_list ]
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# -1 for the stop token
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logits = [ logit[-length-1:-1] for logit, length in zip(logits, slice_lengths) ]
|
||
# greedy sample from the sequence
|
||
refined_list = [ logit.argmax(dim=-1) for logit in logits ]
|
||
# to-do: compare scores
|
||
# set the "refined" list as the output
|
||
sequence_list = refined_list
|
||
|
||
return sequence_list
|
||
|
||
|
||
def example_usage():
|
||
cfg.trainer.backend = "local"
|
||
cfg.hyperparameters.gradient_accumulation_steps = 1
|
||
if cfg.audio_backend == "dac":
|
||
cfg.sample_rate = 44_100
|
||
|
||
from functools import partial
|
||
from einops import repeat
|
||
from tqdm import tqdm
|
||
|
||
from ..emb.qnt import decode_to_file, unload_model, trim_random, repeat_extend_audio, concat_audio, merge_audio
|
||
from ..engines import Engine, Engines
|
||
from ..utils import wrapper as ml
|
||
from ..utils import setup_logging
|
||
|
||
import numpy as np
|
||
import re
|
||
|
||
setup_logging()
|
||
device = "cuda"
|
||
|
||
|
||
# mamba seems to ONLY be used as an AR (any NAR attempts lobotomizes it)
|
||
"""
|
||
if "mamba" in cfg.model.arch_type:
|
||
cfg.model.resp_levels = 1
|
||
"""
|
||
# cfg.model.loss_factors = {}
|
||
|
||
def tokenize(content):
|
||
return torch.tensor( cfg.tokenizer.encode(content) )
|
||
|
||
def _load_quants(path) -> Tensor:
|
||
qnt = np.load(path, allow_pickle=True)[()]
|
||
return torch.from_numpy(qnt["codes"].astype(np.int16))[0, :cfg.model.resp_levels, :].t().to(torch.int16)
|
||
|
||
qnt = _load_quants(f"./data/qnt.{'dac' if cfg.audio_backend == 'dac' else 'enc'}")
|
||
noise = _load_quants(f"./data/noise.{'dac' if cfg.audio_backend == 'dac' else 'enc'}")
|
||
|
||
text_list = [
|
||
tokenize("ˈaɪ wɪl nˌɑːt ˈæsk ɐ sˈɛkənd tˈaɪm").to(device),
|
||
#tokenize("ˈaɪ wɪl nˌɑːt ˈæsk").to(device),
|
||
]
|
||
proms_list = [
|
||
qnt[:cfg.dataset.frames_per_second, :].to(device),
|
||
#qnt[:cfg.dataset.frames_per_second, :].to(device),
|
||
]
|
||
resps_list = [
|
||
qnt[:, :].to(device),
|
||
#qnt[:cfg.dataset.frames_per_second, :].to(device),
|
||
]
|
||
|
||
text_list = text_list[:1]
|
||
proms_list = proms_list[:1]
|
||
resps_list = resps_list[:1]
|
||
|
||
batch_size = len(text_list)
|
||
|
||
# rentet-full is the only configuration with BitNet's BitLinear that converges despite the grad_norm saying otherwise
|
||
kwargs = {
|
||
'n_text_tokens': 256,
|
||
'n_audio_tokens': 1024,
|
||
|
||
'd_model': 1024, # 256, # 1024, # 1536
|
||
'n_heads': 16, # 4, # 16, # 24
|
||
'n_layers': 12, # 32
|
||
'n_experts': 1 if not cfg.model else cfg.model.experts,
|
||
|
||
'p_dropout': 0.1,
|
||
|
||
'l_padding': 8 if cfg.optimizations.fp8 else 0,
|
||
|
||
'config': cfg.model
|
||
}
|
||
|
||
"""
|
||
try:
|
||
kwargs['config'] = cfg.model
|
||
except Exception as e:
|
||
pass
|
||
"""
|
||
|
||
bos_id, space_id, eos_id = cfg.tokenizer.encode( " " )
|
||
#available_tasks = cfg.dataset.tasks_list
|
||
available_tasks = ["tts"] # , "stt"]
|
||
|
||
model = AR_NAR(**kwargs).to(device)
|
||
steps = 500 # 150 * len(available_tasks) # * cfg.model.experimental.causal_size
|
||
|
||
optimizer = cfg.hyperparameters.optimizer.lower() if cfg.yaml_path is not None else "prodigy"
|
||
scheduler = cfg.hyperparameters.scheduler.lower() if cfg.yaml_path is not None else ""
|
||
learning_rate = cfg.hyperparameters.learning_rate if cfg.yaml_path is not None else None
|
||
|
||
if cfg.optimizations.dadaptation:
|
||
# do not combine the two
|
||
if scheduler == "schedulefree":
|
||
scheduler = ""
|
||
|
||
learning_rate = 1.0
|
||
|
||
if optimizer == "prodigy":
|
||
if learning_rate is None:
|
||
learning_rate = 1.0
|
||
|
||
optimizer = ml.Prodigy
|
||
elif optimizer == "adagrad":
|
||
if learning_rate is None:
|
||
learning_rate = 1.0e-2
|
||
|
||
optimizer = ml.Adagrad
|
||
elif optimizer == "adamw":
|
||
if learning_rate is None:
|
||
learning_rate = 1.0e-4
|
||
|
||
optimizer = ml.AdamW
|
||
elif optimizer == "sdg":
|
||
if learning_rate is None:
|
||
learning_rate = 1.0e-4
|
||
|
||
optimizer = ml.SGD
|
||
else:
|
||
raise ValueError(f"Unrecognized optimizer: {optimizer}")
|
||
|
||
_logger.info(f"Optimizer: {optimizer}\tLearning rate: {learning_rate}")
|
||
|
||
optimizer = optimizer(model.parameters(), lr=learning_rate)
|
||
|
||
if scheduler == "schedulefree":
|
||
if isinstance(optimizer, ml.AdamW):
|
||
scheduler = ml.schedulefree.AdamWScheduleFree
|
||
elif isinstance(optimizer, ml.SGD):
|
||
scheduler = ml.schedulefree.SGDScheduleFree
|
||
else:
|
||
scheduler = None
|
||
|
||
if scheduler is not None:
|
||
_logger.info(f"Scheduler: {scheduler}")
|
||
optimizer = scheduler( model.parameters(), lr = learning_rate )
|
||
|
||
if cfg.optimizations.replace and cfg.optimizations.linear:
|
||
model = ml.replace_linear( model )
|
||
|
||
if cfg.optimizations.replace and cfg.optimizations.embedding:
|
||
model = ml.replace_embedding( model )
|
||
|
||
"""
|
||
cfg.optimizations.model_offloading = {
|
||
"devices": ["cuda:0", "cpu"],
|
||
# "limits": [ 0.9, -1 ],
|
||
"assign": [[ f'layers.{i}.' for i in range(0,10) ], [ f'layers.{i}.' for i in range(11,12) ] + [ "model.norm" ]],
|
||
# "limits": [ 256 * (1024 ** 2), -1 ]
|
||
}
|
||
"""
|
||
|
||
engine = Engine(model=model, optimizer=optimizer)
|
||
engines = Engines({"ar+nar": engine})
|
||
engines.setup()
|
||
|
||
"""
|
||
if cfg.optimizations.model_offloading:
|
||
model = ml.offload_model( model, policy=cfg.optimizations.model_offloading )
|
||
"""
|
||
|
||
"""
|
||
torch.save( {
|
||
'module': model.state_dict()
|
||
}, f"./data/{cfg.model.arch_type}.pth" )
|
||
"""
|
||
|
||
_logger.info(f"AR+NAR ({cfg.model.arch_type}, {cfg.audio_backend}) parameter count: {sum(p.numel() for p in model.parameters() if p.requires_grad)}")
|
||
|
||
@torch.no_grad()
|
||
def sample_data(t=None):
|
||
if isinstance(t, list):
|
||
tasks = t
|
||
texts = [ text_list[0].to(device) if task != "stt" else None for i, task in enumerate( tasks ) ]
|
||
proms = [ proms_list[0].to(device) if task != "stt" else [ "stt" ] for i, task in enumerate( tasks ) ]
|
||
resps = [ None if task != "stt" else resps_list[0].to(device) for i, task in enumerate( tasks ) ]
|
||
|
||
return texts, proms, resps, tasks
|
||
|
||
texts = []
|
||
proms = []
|
||
resps = []
|
||
tasks = []
|
||
|
||
for i in range(batch_size):
|
||
task = random.choice(available_tasks) if t is None else t
|
||
|
||
text = text_list[i].to(device)
|
||
prom = proms_list[i].to(device)
|
||
resp = resps_list[i].to(device)
|
||
|
||
# do nothing
|
||
if task == "tts":
|
||
...
|
||
elif task == "stt":
|
||
prom = [
|
||
task
|
||
]
|
||
# to-do: reimplement this from data.py
|
||
"""
|
||
elif task == "tts-c":
|
||
trim_length = int(random.uniform(cfg.dataset.prompt_duration_range[0], cfg.dataset.prompt_duration_range[1]) * cfg.dataset.frames_per_second)
|
||
|
||
prom = resp[:trim_length]
|
||
resp = resp[trim_length:]
|
||
|
||
prom = prom.to(device)
|
||
elif task == "ns" or task == "sr":
|
||
# extend the noise to fill the target audio
|
||
noise_ext = repeat_extend_audio( noise, resp.shape[0] )
|
||
# create the input prompt by merging the target audio with the noise
|
||
prom = merge_audio( resp.cpu(), noise_ext, scale=[1, cfg.dataset.noise_scale], device=cfg.dataset.reencode_device )
|
||
prom = prom.to(device)
|
||
# set the target to just be the noise if <sr>
|
||
if task == "sr":
|
||
resp = noise_ext
|
||
|
||
# set the text prompt to empty to train without a guided text prompt
|
||
if random.random() < 0.5:
|
||
text = torch.tensor([bos_id, eos_id], device=device, dtype=torch.uint8)
|
||
|
||
prom = [
|
||
task,
|
||
prom,
|
||
]
|
||
"""
|
||
|
||
texts.append( text )
|
||
proms.append( prom )
|
||
resps.append( resp )
|
||
tasks.append( task )
|
||
|
||
return texts, proms, resps, tasks
|
||
|
||
@torch.inference_mode()
|
||
def sample( name, steps=500, task=None ):
|
||
engine.eval()
|
||
|
||
texts, proms, resps, tasks = sample_data( task )
|
||
|
||
if "ar" in cfg.model.capabilities:
|
||
output = engine( texts, proms, resps, task_list=tasks, max_steps=steps, sampling_temperature=0.95 )
|
||
|
||
text = [ cfg.tokenizer.decode( output[i] ) for i, task in enumerate( tasks ) if task == "stt" ]
|
||
|
||
texts = [ texts[i] for i, task in enumerate( tasks ) if task != "stt" ]
|
||
proms = [ proms[i] for i, task in enumerate( tasks ) if task != "stt" ]
|
||
resps = [ output[i] for i, task in enumerate( tasks ) if task != "stt" ]
|
||
tasks = [ tasks[i] for i, task in enumerate( tasks ) if task != "stt" ]
|
||
|
||
print( "STT:", text )
|
||
else:
|
||
resps = [ resp[:, 0] for resp in resps ]
|
||
|
||
if "nar" in cfg.model.capabilities:
|
||
resps = engine( texts, proms, resps, task_list=tasks, sampling_temperature=0.2 )
|
||
|
||
for i, o in enumerate(resps):
|
||
_ = decode_to_file(o.to(dtype=torch.int32), f"data/{cfg.model.arch_type}.{cfg.audio_backend}.{i}.{task}.{name}.wav", device=device)
|
||
|
||
unload_model()
|
||
|
||
def train():
|
||
engine.train()
|
||
t = trange(steps)
|
||
for i in t:
|
||
texts, proms, resps, tasks = sample_data()
|
||
|
||
stats = {"step": i}
|
||
stats |= engine.traverse(text_list=texts, proms_list=proms, resps_list=resps, task_list=tasks)
|
||
stats |= {"grad_norm": engine.get_global_grad_norm()}
|
||
|
||
tqdm.write(f"{stats}")
|
||
|
||
"""
|
||
torch.save( {
|
||
'module': model.state_dict()
|
||
}, f"./data/{cfg.model.arch_type}.pth" )
|
||
"""
|
||
|
||
#sample("init", 5)
|
||
train()
|
||
|
||
"""
|
||
if cfg.optimizations.compile:
|
||
model = ml.compile_model(model, backend=cfg.optimizations.compile)
|
||
"""
|
||
|
||
"""
|
||
for task in available_tasks:
|
||
sample("final", task=task)
|
||
"""
|
||
sample("final", task=available_tasks)
|
||
|
||
engines.quit()
|
||
|
||
if __name__ == "__main__":
|
||
example_usage() |