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unslothai--unsloth/tests/saving/text_to_speech_models/test_csm.py
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chore: import upstream snapshot with attribution
2026-07-13 12:59:56 +08:00

162 lines
4.1 KiB
Python

from unsloth import FastLanguageModel, FastModel
from transformers import CsmForConditionalGeneration
import torch
# ruff: noqa
import sys
from pathlib import Path
from peft import PeftModel
import warnings
import requests
REPO_ROOT = Path(__file__).parents[3]
sys.path.insert(0, str(REPO_ROOT))
from tests.utils.cleanup_utils import safe_remove_directory
from tests.utils.os_utils import require_package, require_python_package
require_package("ffmpeg", "ffmpeg")
require_python_package("soundfile")
import soundfile as sf
print(f"\n{'='*80}")
print("🔍 SECTION 1: Loading Model and LoRA Adapters")
print(f"{'='*80}")
model, tokenizer = FastModel.from_pretrained(
model_name = "unsloth/csm-1b",
max_seq_length = 2048,
dtype = None,
auto_model = CsmForConditionalGeneration,
load_in_4bit = False,
)
base_model_class = model.__class__.__name__
model = FastModel.get_peft_model(
model,
r = 32,
target_modules = [
"q_proj",
"k_proj",
"v_proj",
"o_proj",
"gate_proj",
"up_proj",
"down_proj",
],
lora_alpha = 32,
lora_dropout = 0,
bias = "none",
use_gradient_checkpointing = "unsloth",
random_state = 3407,
use_rslora = False,
loftq_config = None,
)
print("✅ Model and LoRA adapters loaded successfully!")
print(f"\n{'='*80}")
print("🔍 SECTION 2: Checking Model Class Type")
print(f"{'='*80}")
assert isinstance(model, PeftModel), "Model should be an instance of PeftModel"
print("✅ Model is an instance of PeftModel!")
print(f"\n{'='*80}")
print("🔍 SECTION 3: Checking Config Model Class Type")
print(f"{'='*80}")
def find_lora_base_model(model_to_inspect):
current = model_to_inspect
if hasattr(current, "base_model"):
current = current.base_model
if hasattr(current, "model"):
current = current.model
return current
config_model = find_lora_base_model(model) if isinstance(model, PeftModel) else model
assert (
config_model.__class__.__name__ == base_model_class
), f"Expected config_model class to be {base_model_class}"
print("✅ config_model returns correct Base Model class:", str(base_model_class))
print(f"\n{'='*80}")
print("🔍 SECTION 4: Saving and Merging Model")
print(f"{'='*80}")
with warnings.catch_warnings():
warnings.simplefilter("error") # treat warnings as errors so saving stays clean
try:
model.save_pretrained_merged("csm", tokenizer)
print("✅ Model saved and merged successfully without warnings!")
except Exception as e:
assert False, f"Model saving/merging failed with exception: {e}"
print(f"\n{'='*80}")
print("🔍 SECTION 5: Loading Model for Inference")
print(f"{'='*80}")
model, processor = FastModel.from_pretrained(
model_name = "./csm",
max_seq_length = 2048,
dtype = None,
auto_model = CsmForConditionalGeneration,
load_in_4bit = False,
)
from transformers import AutoProcessor
processor = AutoProcessor.from_pretrained("unsloth/csm-1b")
print("✅ Model loaded for inference successfully!")
print(f"\n{'='*80}")
print("🔍 SECTION 6: Running Inference")
print(f"{'='*80}")
from transformers import pipeline
import torch
output_audio_path = "csm_audio.wav"
try:
text = "We just finished fine tuning a text to speech model... and it's pretty good!"
speaker_id = 0
inputs = processor(f"[{speaker_id}]{text}", add_special_tokens = True).to("cuda")
audio_values = model.generate(
**inputs,
max_new_tokens = 125, # ~10 seconds of audio
depth_decoder_temperature = 0.6,
depth_decoder_top_k = 0,
depth_decoder_top_p = 0.9,
temperature = 0.8,
top_k = 50,
top_p = 1.0,
output_audio = True,
)
audio = audio_values[0].to(torch.float32).cpu().numpy()
sf.write("example_without_context.wav", audio, 24000)
print(f"✅ Audio generated and saved to {output_audio_path}!")
except Exception as e:
assert False, f"Inference failed with exception: {e}"
print("✅ All sections passed successfully!")
safe_remove_directory("./unsloth_compiled_cache")
safe_remove_directory("./csm")