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bab4072 cd460c3 bab4072 cd460c3 bab4072 cd460c3 bab4072 cd460c3 ba3193a cd460c3 8f6bccc ba3193a cd460c3 bab4072 ba3193a bab4072 ba3193a bab4072 | 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 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 | #!/bin/bash
set -e
export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True
echo '== SETUP =='
apt-get update -qq && apt-get install -y -qq git build-essential ffmpeg >/dev/null 2>&1
git clone -q https://github.com/vibevoice-community/VibeVoice /VibeVoice-classic
git -C /VibeVoice-classic checkout -q d5895e7
git clone -q https://github.com/vibevoice-community/VibeVoice /vv-main
git clone -q https://github.com/voicepowered-ai/VibeVoice-finetuning /vvft
cd /vvft && pip install -q -e . 2>&1 | tail -1
pip uninstall -y -q transformers 2>&1 | tail -1
pip install -q 'setuptools<81' 'torch==2.6.0' 'transformers==4.51.3' 'peft==0.15.2' 'diffusers==0.29.0' 'safetensors==0.5.3' 'datasets==3.6.0' 'resampy==0.4.3' 'librosa==0.11.0' 'numpy<2' 'resemble-perth==1.0.1' 'omegaconf==2.3.0' 'conformer==0.3.2' 's3tokenizer' 'soundfile' 'jiwer' 'resemblyzer' 2>&1 | tail -1
echo '== PREFLIGHT =='
for b in git gcc ffmpeg; do command -v $b >/dev/null || { echo "PREFLIGHT FAIL: missing binary $b"; exit 2; }; done
python - <<'PF'
import importlib
from huggingface_hub import HfApi
for m, v in [("torch", "2.6.0"), ("transformers", "4.51.3"), ("peft", "0.15.2"), ("diffusers", "0.29.0"), ("resemblyzer", None), ("webrtcvad", None)]:
mod = importlib.import_module(m)
if v: assert mod.__version__.split('+')[0] == v, f"{m} {mod.__version__} != {v}"
api = HfApi()
files = api.list_repo_files("ACloudCenter/vibevoice-lora-joint", repo_type="model")
for need in ("fullv3/lora/adapter_config.json", "fullv3/lora/adapter_model.safetensors", "fullv3/lora/diffusion_head_full.bin"):
assert need in files, f"{need} missing"
assert api.repo_exists("ACloudCenter/fast-vibevoice-notes", repo_type="dataset"), "notes missing"
print("PREFLIGHT OK", flush=True)
PF
echo '== DAILYTALK PREP (500 convs) =='
python -c "import urllib.request; urllib.request.urlretrieve('https://huggingface.co/datasets/ACloudCenter/fast-vibevoice-notes/resolve/main/prep_dailytalk.py', '/prep_dailytalk.py'); s=open('/prep_dailytalk.py').read(); assert 'PREP DONE' in s; print('PREP FETCH OK')"
python /prep_dailytalk.py /data 500
echo '== EMO DATA PREP =='
python -c "import urllib.request; urllib.request.urlretrieve('https://huggingface.co/datasets/ACloudCenter/fast-vibevoice-notes/resolve/main/emo_data_prep.py', '/emo_data_prep.py'); s=open('/emo_data_prep.py').read(); assert 'EMO PREP DONE' in s and 'voice_prompts = [' in s; print('EMO FETCH OK')"
python /emo_data_prep.py
echo '== FETCH FULLV3 (warm-start init) =='
python - <<'CK'
import os
from huggingface_hub import HfApi, hf_hub_download
api = HfApi()
sub = "fullv3/lora"
for f in [f for f in api.list_repo_files("ACloudCenter/vibevoice-lora-joint", repo_type="model") if f.startswith(sub + "/")]:
local = hf_hub_download("ACloudCenter/vibevoice-lora-joint", f, repo_type="model")
with open(local, "rb") as fh:
data = fh.read()
op = os.path.join("/fullv3", f[len(sub) + 1:])
os.makedirs(os.path.dirname(op), exist_ok=True)
with open(op, "wb") as fh:
fh.write(data)
assert os.path.isfile(op) and os.path.getsize(op) == len(data)
print("FULLV3 FETCH OK:", sorted(os.listdir("/fullv3")), flush=True)
CK
echo '== WARMSTART PATCH =='
python - <<'WS'
p = "/vvft/src/finetune_vibevoice_lora.py"
s = open(p).read()
a1 = " model.model.language_model = get_peft_model(model.model.language_model, lora_cfg)"
assert a1 in s, "lm anchor missing"
s = s.replace(a1, a1 + """
if os.path.isfile("/fullv3/adapter_config.json"):
import importlib.util as _ilu2
import sys as _sys2
from pathlib import Path as _P
_spec2 = _ilu2.spec_from_file_location("lora_loading_ws", "/vv-main/vibevoice/modular/lora_loading.py")
_mod2 = _ilu2.module_from_spec(_spec2)
_sys2.modules[_spec2.name] = _mod2
_spec2.loader.exec_module(_mod2)
_rep = _mod2._LoadReport()
_dev = next(model.parameters()).device
_mod2._load_language_model(model, _P("/fullv3"), _dev, _rep)
print(f"WARMSTART LM via peft loader: lm={_rep.language_model}", flush=True)""")
a2 = """ if getattr(model_args, "train_diffusion_head", False) and hasattr(model.model, "prediction_head"):
for p in model.model.prediction_head.parameters():
p.requires_grad = True"""
assert a2 in s, "head anchor missing"
s = s.replace(a2, a2 + """
if os.path.isfile("/fullv3/diffusion_head_full.bin"):
_hsd = torch.load("/fullv3/diffusion_head_full.bin", map_location="cpu")
_hm, _hu = model.model.prediction_head.load_state_dict(_hsd, strict=False)
print(f"WARMSTART HEAD: missing={len(_hm)} unexpected={len(_hu)}", flush=True)
for _cname in ("acoustic_connector", "semantic_connector"):
_cp = f"/fullv3/{_cname}/pytorch_model.bin"
if os.path.isfile(_cp):
getattr(model.model, _cname).load_state_dict(torch.load(_cp, map_location="cpu"), strict=False)
print(f"WARMSTART {_cname} OK", flush=True)""")
open(p, "w").write(s)
print("WARMSTART PATCH OK (peft-loader LM init, sys bound)", flush=True)
WS
echo '== TRAIN (emotion-LoRA warm-start, 800 steps) =='
python -m src.finetune_vibevoice_lora \
--model_name_or_path microsoft/VibeVoice-1.5B \
--processor_name_or_path src/vibevoice/processor \
--train_jsonl /data/mixed_train.jsonl --validation_jsonl /data/mixed_val.jsonl \
--output_dir out_emo --per_device_train_batch_size 1 --gradient_accumulation_steps 16 \
--max_steps 800 --learning_rate 1e-5 --logging_steps 10 --save_steps 200 --save_total_limit 2 \
--eval_strategy steps --eval_steps 200 --do_eval \
--load_best_model_at_end --metric_for_best_model eval_loss --greater_is_better False \
--label_names input_ids \
--remove_unused_columns False --bf16 True --do_train --gradient_clipping \
--gradient_checkpointing True --gradient_checkpointing_kwargs '{"use_reentrant": false}' \
--ddpm_batch_mul 2 --diffusion_loss_weight 1.4 --train_diffusion_head True --ce_loss_weight 0.04 \
--voice_prompt_drop_rate 0.2 \
--lora_target_modules q_proj,k_proj,v_proj,o_proj,gate_proj,up_proj,down_proj \
--lr_scheduler_type cosine --warmup_ratio 0.03 --max_grad_norm 0.8 --report_to none \
2>&1 | tee /tmp/joblog.txt
echo '== PUSH =='
python - <<'PUSH'
from huggingface_hub import HfApi
import glob, os
api = HfApi()
n = 0
for p in glob.glob('out_emo/**/*', recursive=True):
if os.path.isfile(p):
api.upload_file(path_or_fileobj=p, path_in_repo='emo3/' + os.path.relpath(p, 'out_emo'), repo_id='ACloudCenter/vibevoice-lora-joint')
n += 1
print(f'PUSH OK ({n} files)', flush=True)
PUSH
python - <<'LOG'
from huggingface_hub import HfApi
try:
HfApi().upload_file(path_or_fileobj="/tmp/joblog.txt", path_in_repo="emo-train-log-v4.txt", repo_id="ACloudCenter/fast-vibevoice-notes", repo_type="dataset")
print("LOG OK", flush=True)
except Exception as e:
print("LOG FAIL", e, flush=True)
LOG |