711 lines
30 KiB
Python
711 lines
30 KiB
Python
import copy
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import logging
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import os
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import queue
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import statistics
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import threading
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import time
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from functools import lru_cache
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from pathlib import Path
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import safetensors
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import torch
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from torch import nn
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from torch.nn import functional as F
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from typing_extensions import Self
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from pocket_tts.conditioners.base import TokenizedText
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from pocket_tts.data.audio import audio_read
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from pocket_tts.data.audio_utils import convert_audio
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from pocket_tts.default_parameters import (
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DEFAULT_EOS_THRESHOLD,
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DEFAULT_LSD_DECODE_STEPS,
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DEFAULT_NOISE_CLAMP,
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DEFAULT_TEMPERATURE,
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DEFAULT_VARIANT,
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)
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from pocket_tts.models.flow_lm import FlowLMModel
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from pocket_tts.models.mimi import MimiModel
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from pocket_tts.modules import mimi_transformer
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from pocket_tts.modules.dummy_quantizer import DummyQuantizer
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from pocket_tts.modules.seanet import SEANetDecoder, SEANetEncoder
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from pocket_tts.modules.stateful_module import increment_steps, init_states
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from pocket_tts.utils.config import Config, load_config
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from pocket_tts.utils.utils import (
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PREDEFINED_VOICES,
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display_execution_time,
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download_if_necessary,
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load_predefined_voice,
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size_of_dict,
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)
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from pocket_tts.utils.weights_loading import get_flow_lm_state_dict, get_mimi_state_dict
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torch.set_num_threads(1)
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logger = logging.getLogger(__name__)
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class TTSModel(nn.Module):
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def __init__(
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self,
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flow_lm: FlowLMModel,
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temp: float,
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lsd_decode_steps: int,
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noise_clamp: float | None,
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eos_threshold,
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config: Config,
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):
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super().__init__()
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self.flow_lm = flow_lm
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self.temp = temp
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self.lsd_decode_steps = lsd_decode_steps
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self.noise_clamp = noise_clamp
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self.eos_threshold = eos_threshold
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self.config = config
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self.has_voice_cloning = True
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@property
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def device(self) -> str:
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return next(self.parameters()).device.type
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@property
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def sample_rate(self) -> int:
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return self.config.mimi.sample_rate
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@classmethod
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def _from_pydantic_config(
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cls, config: Config, temp, lsd_decode_steps, noise_clamp: float | None, eos_threshold
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) -> Self:
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flow_lm = FlowLMModel.from_pydantic_config(
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config.flow_lm, latent_dim=config.mimi.quantizer.dimension
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)
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tts_model = cls(flow_lm, temp, lsd_decode_steps, noise_clamp, eos_threshold, config)
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return tts_model
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@classmethod
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def _from_pydantic_config_with_weights(
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cls, config: Config, temp, lsd_decode_steps, noise_clamp: float | None, eos_threshold
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) -> Self:
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tts_model = cls._from_pydantic_config(
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config, temp, lsd_decode_steps, noise_clamp, eos_threshold
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)
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tts_model.flow_lm.speaker_proj_weight = torch.nn.Parameter(
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torch.zeros((1024, 512), dtype=torch.float32)
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)
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if config.flow_lm.weights_path is not None:
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if config.mimi.weights_path is None:
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raise ValueError(
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"If you specify flow_lm.weights_path you should specify mimi.weights_path"
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)
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logger.info(f"Loading FlowLM weights from {config.flow_lm.weights_path}")
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state_dict_flowlm = get_flow_lm_state_dict(
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download_if_necessary(config.flow_lm.weights_path)
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)
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tts_model.flow_lm.load_state_dict(state_dict_flowlm, strict=True)
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# safetensors.torch.save_file(tts_model.state_dict(), "7442637a.safetensors")
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# Create mimi config directly from the provided config using model_dump
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mimi_config = config.mimi.model_dump()
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# Build mimi model from config
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encoder = SEANetEncoder(**mimi_config["seanet"])
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decoder = SEANetDecoder(**mimi_config["seanet"])
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encoder_transformer = mimi_transformer.ProjectedTransformer(**mimi_config["transformer"])
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decoder_transformer = mimi_transformer.ProjectedTransformer(**mimi_config["transformer"])
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quantizer = DummyQuantizer(**mimi_config["quantizer"])
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tts_model.mimi = MimiModel(
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encoder,
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decoder,
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quantizer,
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channels=mimi_config["channels"],
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sample_rate=mimi_config["sample_rate"],
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frame_rate=mimi_config["frame_rate"],
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encoder_frame_rate=mimi_config["sample_rate"] / encoder.hop_length,
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encoder_transformer=encoder_transformer,
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decoder_transformer=decoder_transformer,
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).to(device="cpu")
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# Load mimi weights from the config safetensors file with complete mapping for strict loading
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if config.mimi.weights_path is not None:
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if config.flow_lm.weights_path is None:
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raise ValueError(
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"If you specify mimi.weights_path you should specify flow_lm.weights_path"
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)
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logger.info(f"Loading Mimi weights from {config.mimi.weights_path}")
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mimi_state = get_mimi_state_dict(download_if_necessary(config.mimi.weights_path))
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tts_model.mimi.load_state_dict(mimi_state, strict=True)
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tts_model.mimi.eval()
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# tts_model.to(dtype=torch.float32)
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# uncomment to save the weights
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# tts_model = tts_model.to(dtype=torch.bfloat16)
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# safetensors.torch.save_file(tts_model.state_dict(), "tts_b6369a24.safetensors")
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if config.weights_path is not None:
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logger.info(f"Loading TTSModel weights from {config.weights_path}")
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try:
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weights_file = download_if_necessary(config.weights_path)
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except Exception:
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tts_model.has_voice_cloning = False
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weights_file = download_if_necessary(config.weights_path_without_voice_cloning)
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state_dict = safetensors.torch.load_file(weights_file)
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tts_model.load_state_dict(state_dict, strict=True)
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if config.flow_lm.weights_path is None and config.weights_path is None:
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logger.warning(
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"No weights_path specified for FlowLM or TTSModel, model is uninitialized!"
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)
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size_in_mb = size_of_dict(tts_model.state_dict()) // 1e6
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logging.info(f"TTS Model loaded successfully. Its size is {size_in_mb} MB")
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return tts_model
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def load_model(
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variant: str = DEFAULT_VARIANT,
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temp: float | int = DEFAULT_TEMPERATURE,
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lsd_decode_steps: int = DEFAULT_LSD_DECODE_STEPS,
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noise_clamp: float | int | None = DEFAULT_NOISE_CLAMP,
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eos_threshold: float = DEFAULT_EOS_THRESHOLD,
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) -> Self:
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"""Load a pre-trained TTS model with specified configuration.
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This class method loads a complete TTS model including the flow language model
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and Mimi compression model from pre-trained weights. The model is initialized
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with the specified generation parameters and ready for inference.
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Args:
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variant: Model variant identifier corresponding to a config file name
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(e.g., '610b0b2c'). Must match a YAML file in the config directory.
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temp: Sampling temperature for generation. Higher values produce more
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diverse but potentially lower quality output.
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lsd_decode_steps: Number of steps for Lagrangian Self Distillation
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decoding. More steps can improve quality but increase computation.
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noise_clamp: Maximum value for noise sampling. If None, no clamping
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is applied. Helps prevent extreme values in generation.
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eos_threshold: Threshold for end-of-sequence detection. Higher values
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make the model more likely to continue generating.
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Returns:
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TTSModel: Fully initialized model with loaded weights on cpu, ready for
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text-to-speech generation.
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Raises:
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FileNotFoundError: If the specified config file or model weights
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are not found.
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ValueError: If the configuration is invalid or incompatible.
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"""
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config = load_config(Path(__file__).parents[1] / f"config/{variant}.yaml")
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tts_model = TTSModel._from_pydantic_config_with_weights(
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config, temp, lsd_decode_steps, noise_clamp, eos_threshold
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)
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return tts_model
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def _run_flow_lm_and_increment_step(
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self,
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model_state: dict,
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text_tokens: torch.Tensor | None = None,
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backbone_input_latents: torch.Tensor | None = None,
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audio_conditioning: torch.Tensor | None = None,
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) -> tuple[torch.Tensor, torch.Tensor]:
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"""First one is the backbone output, second one is the audio decoding output."""
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if text_tokens is None:
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text_tokens = torch.zeros((1, 0), dtype=torch.int64, device=self.flow_lm.device)
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if backbone_input_latents is None:
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backbone_input_latents = torch.empty(
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(1, 0, self.flow_lm.ldim), dtype=self.flow_lm.dtype, device=self.flow_lm.device
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)
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if audio_conditioning is None:
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audio_conditioning = torch.empty(
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(1, 0, self.flow_lm.dim), dtype=self.flow_lm.dtype, device=self.flow_lm.device
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)
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output = self._run_flow_lm(
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text_tokens=text_tokens,
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backbone_input_latents=backbone_input_latents,
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model_state=model_state,
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audio_conditioning=audio_conditioning,
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)
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increment_by = (
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text_tokens.shape[1] + backbone_input_latents.shape[1] + audio_conditioning.shape[1]
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)
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increment_steps(self.flow_lm, model_state, increment=increment_by)
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return output
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def _run_flow_lm(
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self,
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model_state: dict,
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text_tokens: torch.Tensor,
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backbone_input_latents: torch.Tensor,
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audio_conditioning: torch.Tensor,
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) -> tuple[torch.Tensor, torch.Tensor]:
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text_embeddings = self.flow_lm.conditioner(TokenizedText(text_tokens))
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text_embeddings = torch.cat([text_embeddings, audio_conditioning], dim=1)
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output_embeddings, is_eos = self.flow_lm._sample_next_latent(
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backbone_input_latents,
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text_embeddings,
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model_state=model_state,
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lsd_decode_steps=self.lsd_decode_steps,
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temp=self.temp,
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noise_clamp=self.noise_clamp,
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eos_threshold=self.eos_threshold,
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)
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return output_embeddings[:, None, :], is_eos
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def _encode_audio(self, audio: torch.Tensor) -> torch.Tensor:
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encoded = self.mimi.encode_to_latent(audio)
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latents = encoded.transpose(-1, -2).to(torch.float32)
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conditioning = F.linear(latents, self.flow_lm.speaker_proj_weight)
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return conditioning
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def _decode_audio_worker(self, latents_queue: queue.Queue, result_queue: queue.Queue):
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"""Worker thread function for decoding audio latents from queue with immediate streaming."""
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try:
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audio_chunks = []
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mimi_state = init_states(self.mimi, batch_size=1, sequence_length=1000)
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while True:
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latent = latents_queue.get()
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if latent is None:
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break
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mimi_decoding_input = latent * self.flow_lm.emb_std + self.flow_lm.emb_mean
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transposed = mimi_decoding_input.transpose(-1, -2)
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quantized = self.mimi.quantizer(transposed)
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t = time.monotonic()
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audio_frame = self.mimi.decode_from_latent(quantized, mimi_state)
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increment_steps(self.mimi, mimi_state, increment=16)
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audio_frame_duration = audio_frame.shape[2] / self.config.mimi.sample_rate
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# We could log the timings here.
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logger.debug(
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" " * 30 + "Decoded %d ms of audio with mimi in %d ms",
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int(audio_frame_duration * 1000),
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int((time.monotonic() - t) * 1000),
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)
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audio_chunks.append(audio_frame)
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result_queue.put(("chunk", audio_frame))
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latents_queue.task_done()
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# Signal completion
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result_queue.put(("done", None))
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except Exception as e:
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# Put error in result queue
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result_queue.put(("error", e))
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@torch.no_grad
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def generate_audio(
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self,
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model_state: dict,
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text_to_generate: str,
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frames_after_eos: int | None = None,
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copy_state: bool = True,
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) -> torch.Tensor:
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"""Generate complete audio tensor from text input.
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This method generates the full audio output for the given text prompt
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and returns it as a single tensor. It internally uses the streaming
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generation method but collects all chunks before returning.
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This method is NOT thread-safe; separate model instances should be used
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for concurrent generation.
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Args:
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model_state: Model state dictionary containing hidden states and
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positional information. Can be obtained from get_state_for_audio_prompt()
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or init_states(). The state may be modified during generation.
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text_to_generate: Input text to convert to speech. The text will be
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automatically formatted (capitalization, punctuation) for optimal
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generation quality.
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frames_after_eos: Number of additional frames to generate after
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detecting end-of-sequence. If None, automatically determined
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based on text length (1-3 frames).
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copy_state: Whether to create a deep copy of the model state before
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generation. If True, preserves the original state for reuse.
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If False, modifies the input state in-place. Defaults to True.
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Returns:
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torch.Tensor: Generated audio tensor with shape [channels, samples]
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at the model's sample rate (typically 24kHz). The audio is
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normalized and ready for playback or saving.
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You can get the sample rate from the `sample_rate` attribute.
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Raises:
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ValueError: If text_to_generate is empty or invalid.
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RuntimeError: If generation fails due to model errors.
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"""
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audio_chunks = []
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for chunk in self.generate_audio_stream(
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model_state=model_state,
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text_to_generate=text_to_generate,
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frames_after_eos=frames_after_eos,
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copy_state=copy_state,
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):
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audio_chunks.append(chunk)
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return torch.cat(audio_chunks, dim=0)
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@torch.no_grad
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def generate_audio_stream(
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self,
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model_state: dict,
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text_to_generate: str,
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frames_after_eos: int | None = None,
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copy_state: bool = True,
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):
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"""Generate audio streaming chunks from text input.
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This method generates audio from text and yields chunks as they become
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available, enabling real-time playback or processing. It uses multithreading
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to parallelize generation and decoding for optimal performance.
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This method is NOT thread-safe; separate model instances should be used
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for concurrent generation.
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Args:
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model_state: Model state dictionary containing hidden states and
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positional information. Can be obtained from get_state_for_audio_prompt()
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or init_states(). The state may be modified during generation.
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text_to_generate: Input text to convert to speech. The text will be
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automatically formatted (capitalization, punctuation) for optimal
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generation quality.
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frames_after_eos: Number of additional frames to generate after
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detecting end-of-sequence. If None, automatically determined
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based on text length (1-3 frames). Defaults to None.
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copy_state: Whether to create a deep copy of the model state before
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generation. If True, preserves the original state for reuse.
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If False, modifies the input state in-place. Defaults to True.
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Yields:
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torch.Tensor: Audio chunks with shape [samples] at the model's
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sample rate (typically 24kHz). Chunks are yielded as soon as
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they are decoded, enabling real-time streaming.
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Raises:
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ValueError: If text_to_generate is empty or invalid.
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RuntimeError: If generation fails due to model errors or threading issues.
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Note:
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This method uses multithreading to parallelize latent generation
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and audio decoding. Generation performance is logged including
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real-time factor (RTF) metrics.
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"""
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# This is a very simplistic way of handling long texts. We could do much better
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# by using teacher forcing, but it would be a bit slower.
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# TODO: add the teacher forcing method for long texts where we use the audio of one chunk
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# as conditioning for the next chunk.
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chunks = split_into_best_sentences(self.flow_lm.conditioner.tokenizer, text_to_generate)
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for chunk in chunks:
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text_to_generate, frames_after_eos_guess = prepare_text_prompt(chunk)
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frames_after_eos_guess += 2
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yield from self._generate_audio_stream_short_text(
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model_state=model_state,
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text_to_generate=chunk,
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frames_after_eos=frames_after_eos_guess,
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copy_state=copy_state,
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)
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@torch.no_grad
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def _generate_audio_stream_short_text(
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self, model_state: dict, text_to_generate: str, frames_after_eos: int, copy_state: bool
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):
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if copy_state:
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model_state = copy.deepcopy(model_state)
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# Set up multithreaded generation and decoding
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latents_queue = queue.Queue()
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result_queue = queue.Queue()
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# Start decoder worker thread
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decoder_thread = threading.Thread(
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target=self._decode_audio_worker, args=(latents_queue, result_queue), daemon=True
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)
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logger.info("starting timer now!")
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t_generating = time.monotonic()
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decoder_thread.start()
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# Generate latents and add them to queue (decoder processes them in parallel)
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self._generate(
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model_state=model_state,
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text_to_generate=text_to_generate,
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frames_after_eos=frames_after_eos,
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latents_queue=latents_queue,
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result_queue=result_queue,
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)
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# Stream audio chunks as they become available
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total_generated_samples = 0
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while True:
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result = result_queue.get()
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if result[0] == "chunk":
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# Audio chunk available immediately for streaming/playback
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audio_chunk = result[1]
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total_generated_samples += audio_chunk.shape[-1]
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yield audio_chunk[0, 0] # Remove batch, channel
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elif result[0] == "done":
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# Generation complete
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break
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elif result[0] == "error":
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# Wait for decoder thread to finish cleanly before propagating error
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with display_execution_time("Waiting for mimi decoder to finish"):
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decoder_thread.join()
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# Propagate error
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raise result[1]
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# Wait for decoder thread to finish cleanly
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with display_execution_time("Waiting for mimi decoder to finish"):
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decoder_thread.join()
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# Print timing information
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duration_generated_audio = int(
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total_generated_samples * 1000 / self.config.mimi.sample_rate
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)
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generation_time = int((time.monotonic() - t_generating) * 1000)
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real_time_factor = duration_generated_audio / generation_time
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|
|
logger.info(
|
|
"Generated: %d ms of audio in %d ms so %.2fx faster than real-time",
|
|
duration_generated_audio,
|
|
generation_time,
|
|
real_time_factor,
|
|
)
|
|
|
|
@torch.no_grad
|
|
def _generate(
|
|
self,
|
|
model_state: dict,
|
|
text_to_generate: str,
|
|
frames_after_eos: int,
|
|
latents_queue: queue.Queue,
|
|
result_queue: queue.Queue,
|
|
):
|
|
gen_len_sec = len(text_to_generate.split()) * 1 + 2.0
|
|
max_gen_len = int(gen_len_sec * 12.5)
|
|
prepared = self.flow_lm.conditioner.prepare(text_to_generate)
|
|
|
|
with display_execution_time("Prompting text"):
|
|
self._run_flow_lm_and_increment_step(
|
|
model_state=model_state, text_tokens=prepared.tokens
|
|
)
|
|
|
|
def run_generation():
|
|
try:
|
|
self._autoregressive_generation(
|
|
model_state, max_gen_len, frames_after_eos, latents_queue
|
|
)
|
|
except Exception as e:
|
|
logger.error(f"Error in autoregressive generation: {e}")
|
|
# Signal decoder to stop by putting None (completion sentinel)
|
|
if latents_queue is not None:
|
|
latents_queue.put(None)
|
|
# Report error to main thread
|
|
if result_queue is not None:
|
|
result_queue.put(("error", e))
|
|
|
|
generation_thread = threading.Thread(target=run_generation, daemon=True)
|
|
generation_thread.start()
|
|
|
|
def _autoregressive_generation(
|
|
self, model_state: dict, max_gen_len: int, frames_after_eos: int, latents_queue: queue.Queue
|
|
):
|
|
backbone_input = torch.full(
|
|
(1, 1, self.flow_lm.ldim),
|
|
fill_value=float("NaN"),
|
|
device=next(iter(self.flow_lm.parameters())).device,
|
|
dtype=self.flow_lm.dtype,
|
|
)
|
|
steps_times = []
|
|
eos_step = None
|
|
for generation_step in range(max_gen_len):
|
|
with display_execution_time("Generating latent", print_output=False) as timer:
|
|
next_latent, is_eos = self._run_flow_lm_and_increment_step(
|
|
model_state=model_state, backbone_input_latents=backbone_input
|
|
)
|
|
if is_eos.item() and eos_step is None:
|
|
eos_step = generation_step
|
|
if eos_step is not None and generation_step >= eos_step + frames_after_eos:
|
|
break
|
|
|
|
# Add generated latent to queue for immediate decoding
|
|
latents_queue.put(next_latent)
|
|
backbone_input = next_latent
|
|
steps_times.append(timer.elapsed_time_ms)
|
|
else:
|
|
if os.environ.get("KPOCKET_TTS_ERROR_WITHOUT_EOS", "0") == "1":
|
|
raise RuntimeError("Generation reached maximum length without EOS!")
|
|
logger.warning(
|
|
"Maximum generation length reached without EOS, this very often indicates an error."
|
|
)
|
|
|
|
# Add sentinel value to signal end of generation
|
|
latents_queue.put(None)
|
|
logger.info("Average generation step time: %d ms", int(statistics.mean(steps_times)))
|
|
|
|
@lru_cache(maxsize=2)
|
|
def _cached_get_state_for_audio_prompt(
|
|
self, audio_conditioning: Path | str | torch.Tensor, truncate: bool = False
|
|
) -> dict:
|
|
return self.get_state_for_audio_prompt(audio_conditioning, truncate)
|
|
|
|
@torch.no_grad
|
|
def get_state_for_audio_prompt(
|
|
self, audio_conditioning: Path | str | torch.Tensor, truncate: bool = False
|
|
) -> dict:
|
|
"""Create model state conditioned on audio prompt for continuation.
|
|
|
|
This method processes an audio prompt and creates a model state that
|
|
captures the acoustic characteristics (speaker voice, style, prosody)
|
|
for use in subsequent text-to-speech generation. The resulting state
|
|
enables voice cloning and audio continuation with speaker consistency.
|
|
|
|
Args:
|
|
audio_conditioning: Audio prompt to condition on. Can be:
|
|
- Path: Local file path to audio file
|
|
- str: URL to download audio file from
|
|
- torch.Tensor: Pre-loaded audio tensor with shape [channels, samples]
|
|
truncate: Whether to truncate long audio prompts to 30 seconds.
|
|
Helps prevent memory issues with very long inputs. Defaults to False.
|
|
|
|
Returns:
|
|
dict: Model state dictionary containing hidden states and positional
|
|
information conditioned on the audio prompt. This state can be
|
|
passed to `generate_audio()` or `generate_audio_stream()` for
|
|
voice-consistent generation.
|
|
|
|
Raises:
|
|
FileNotFoundError: If audio file path doesn't exist.
|
|
ValueError: If audio tensor is invalid or empty.
|
|
RuntimeError: If audio processing or encoding fails.
|
|
|
|
Note:
|
|
- Audio is automatically resampled to the model's sample rate (24kHz)
|
|
- The audio is encoded using the Mimi compression model and projected
|
|
to the flow model's latent space
|
|
- Processing time is logged for performance monitoring
|
|
- The state preserves speaker characteristics for voice cloning
|
|
"""
|
|
if isinstance(audio_conditioning, str) and audio_conditioning in PREDEFINED_VOICES:
|
|
# We get the audio conditioning directly from the safetensors file.
|
|
prompt = load_predefined_voice(audio_conditioning)
|
|
else:
|
|
if not self.has_voice_cloning and isinstance(audio_conditioning, (str, Path)):
|
|
raise ValueError(
|
|
f"We could not download the weights for the model with voice cloning, "
|
|
f"but you're trying to use voice cloning. "
|
|
f"Without voice cloning, you can use our catalog of voices {list(PREDEFINED_VOICES)}. "
|
|
f"If you want access to the model with voice cloning, go to "
|
|
f"https://huggingface.co/kyutai/pocket-tts and accept the terms, "
|
|
f"then make sure you're logged in locally with `uvx hf auth login`."
|
|
)
|
|
if isinstance(audio_conditioning, str):
|
|
audio_conditioning = download_if_necessary(audio_conditioning)
|
|
|
|
if isinstance(audio_conditioning, Path):
|
|
audio, conditioning_sample_rate = audio_read(audio_conditioning)
|
|
|
|
if truncate:
|
|
max_samples = int(30 * conditioning_sample_rate) # 30 seconds of audio
|
|
if audio.shape[-1] > max_samples:
|
|
audio = audio[..., :max_samples]
|
|
logger.info(f"Audio truncated to first 30 seconds ({max_samples} samples)")
|
|
|
|
audio_conditioning = convert_audio(
|
|
audio, conditioning_sample_rate, self.config.mimi.sample_rate, 1
|
|
)
|
|
|
|
with display_execution_time("Encoding audio prompt"):
|
|
prompt = self._encode_audio(audio_conditioning.unsqueeze(0).to(self.device))
|
|
# import safetensors.torch
|
|
# safetensors.torch.save_file({"audio_prompt": prompt}, "embeddings/eponine.safetensors")
|
|
|
|
model_state = init_states(self.flow_lm, batch_size=1, sequence_length=1000)
|
|
|
|
with display_execution_time("Prompting audio"):
|
|
self._run_flow_lm_and_increment_step(model_state=model_state, audio_conditioning=prompt)
|
|
|
|
return model_state
|
|
|
|
|
|
def prepare_text_prompt(text: str) -> tuple[str, int]:
|
|
text = text.strip()
|
|
if text == "":
|
|
raise ValueError("Text prompt cannot be empty")
|
|
text = text.replace("\n", " ").replace("\r", " ").replace(" ", " ")
|
|
number_of_words = len(text.split())
|
|
if number_of_words <= 4:
|
|
frames_after_eos_guess = 3
|
|
else:
|
|
frames_after_eos_guess = 1
|
|
|
|
# Make sure it starts with an uppercase letter
|
|
if not text[0].isupper():
|
|
text = text[0].upper() + text[1:]
|
|
|
|
# Let's make sure it ends with some kind of punctuation
|
|
# If it ends with a letter or digit, we add a period.
|
|
if text[-1].isalnum():
|
|
text = text + "."
|
|
|
|
# The model does not perform well when there are very few tokens, so
|
|
# we can add empty spaces at the beginning to increase the token count.
|
|
if len(text.split()) < 5:
|
|
text = " " * 8 + text
|
|
|
|
return text, frames_after_eos_guess
|
|
|
|
|
|
def split_into_best_sentences(tokenizer, text_to_generate: str) -> list[str]:
|
|
text_to_generate, _ = prepare_text_prompt(text_to_generate)
|
|
text_to_generate = text_to_generate.strip()
|
|
tokens = tokenizer(text_to_generate)
|
|
list_of_tokens = tokens.tokens[0].tolist()
|
|
|
|
_, *end_of_sentence_tokens = tokenizer(".!...?").tokens[0].tolist()
|
|
|
|
end_of_sentences_indices = [0]
|
|
previous_was_end_of_sentence_token = False
|
|
|
|
for token_idx, token in enumerate(list_of_tokens):
|
|
if token in end_of_sentence_tokens:
|
|
previous_was_end_of_sentence_token = True
|
|
else:
|
|
if previous_was_end_of_sentence_token:
|
|
end_of_sentences_indices.append(token_idx)
|
|
previous_was_end_of_sentence_token = False
|
|
end_of_sentences_indices.append(len(list_of_tokens))
|
|
|
|
nb_tokens_and_sentences = []
|
|
for i in range(len(end_of_sentences_indices) - 1):
|
|
# let's print
|
|
start = end_of_sentences_indices[i]
|
|
end = end_of_sentences_indices[i + 1]
|
|
text = tokenizer.sp.decode(list_of_tokens[start:end])
|
|
nb_tokens_and_sentences.append((end - start, text))
|
|
|
|
max_nb_tokens_in_a_chunk = 50
|
|
chunks = []
|
|
current_chunk = ""
|
|
current_nb_of_tokens_in_chunk = 0
|
|
for nb_tokens, sentence in nb_tokens_and_sentences:
|
|
if current_chunk == "":
|
|
current_chunk = sentence
|
|
current_nb_of_tokens_in_chunk = nb_tokens
|
|
continue
|
|
|
|
if current_nb_of_tokens_in_chunk + nb_tokens > max_nb_tokens_in_a_chunk:
|
|
chunks.append(current_chunk.strip())
|
|
current_chunk = sentence
|
|
current_nb_of_tokens_in_chunk = nb_tokens
|
|
else:
|
|
current_chunk += " " + sentence
|
|
current_nb_of_tokens_in_chunk += nb_tokens
|
|
|
|
if current_chunk != "":
|
|
chunks.append(current_chunk.strip())
|
|
|
|
return chunks
|