File size: 2,827 Bytes
4f93701 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 | from dataclasses import dataclass, field
from typing import List, Optional
@dataclass
class IndicTrainConfig:
# Base Chatterbox model files downloaded by setup.py.
model_dir: str = "./pretrained_models"
# Dataset layout. Mixed-language metadata should include a language column:
# filename|raw_text|normalized_text|language_id
csv_path: str = "./IndicFinetuning/datasets/MalayalamDataset/metadata.csv"
metadata_path: str = "./IndicFinetuning/datasets/metadata.json"
wav_dir: str = "./IndicFinetuning/datasets/MalayalamDataset/wavs"
preprocessed_dir: str = "./IndicFinetuning/datasets/MalayalamDataset/preprocess"
output_dir: str = "./IndicFinetuning/outputs"
tokenizer_path: str = "./IndicFinetuning/tokenizer/tokenizer_indic.json"
# Model selection.
is_turbo: bool = False
is_lora: bool = True
# Toggle languages here. For single-language Malayalam training, keep ["ml"].
target_languages: List[str] = field(default_factory=lambda: ["ml"])
default_language: str = "ml"
metadata_language_column: Optional[int] = 3
add_language_tag: bool = True
normalize_unicode: str = "NFC"
# Dataset format.
ljspeech: bool = True
json_format: bool = False
preprocess: bool = True
# Inference smoke test.
is_inference: bool = False
inference_language: str = "ml"
inference_prompt_path: str = "/workspace/Indic-ChatterBox/IndicFinetuning/outputs/reference_trimmed.wav"
inference_test_text: str = "പ്രണവേ എനിക്ക് നിന്നെ കാണാൻ really തോന്നുന്നു ഇന്ന് whole day mind full of thoughts ആയിരുന്നു നീ എവിടെയാ, എന്താ doing എന്ന് constantly ഓർമ്മ വരുന്നു just come back once, എനിക്ക് സംസാരിക്കണം നിന്നോട്"
# Vocabulary. Update after building the Indic tokenizer.
new_vocab_size: int = 2573
# LoRA.
lora_r: int = 128
lora_alpha: int = 256
lora_target_modules: List[str] = field(default_factory=lambda: ["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj", "spkr_enc"])
turbo_lora_target_modules: List[str] = field(default_factory=lambda: ["c_attn", "c_proj", "c_fc", "spkr_enc"])
lora_modules_to_save: List[str] = field(default_factory=lambda: ["text_emb", "text_head"])
# Training.
batch_size: int = 16
grad_accum: int = 1
learning_rate: float = 1e-4
num_epochs: int = 10
save_steps: int = 500
save_total_limit: int = 5
dataloader_num_workers: int = 8
# Sequence constraints.
start_text_token: int = 255
stop_text_token: int = 0
max_text_len: int = 256
max_speech_len: int = 850
prompt_duration: float = 3.0
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