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"""
Maya1 Model Loader
Loads Maya1 model with vLLM engine and validates emotion tags.
"""
import os
from transformers import AutoTokenizer
from vllm import AsyncLLMEngine, AsyncEngineArgs, SamplingParams
from .constants import (
ALL_EMOTION_TAGS,
DEFAULT_MAX_MODEL_LEN,
SOH_ID, EOH_ID, SOA_ID, BOS_ID, TEXT_EOT_ID, CODE_START_TOKEN_ID,
)
class Maya1Model:
"""Maya1 TTS Model with vLLM inference engine."""
def __init__(
self,
model_path: str = None,
dtype: str = "bfloat16",
max_model_len: int = DEFAULT_MAX_MODEL_LEN,
gpu_memory_utilization: float = 0.85,
tensor_parallel_size: int = 1,
**engine_kwargs
):
"""
Initialize Maya1 model with vLLM.
Args:
model_path: Path to checkpoint (local or HF repo)
dtype: Model precision (bfloat16 recommended)
max_model_len: Maximum sequence length
gpu_memory_utilization: GPU memory fraction
tensor_parallel_size: Number of GPUs
"""
# Use provided path or environment variable or default
if model_path is None:
model_path = os.environ.get(
'MAYA1_MODEL_PATH',
os.path.expanduser('~/models/maya1-voice')
)
self.model_path = model_path
self.dtype = dtype
print(f"Initializing Maya1 Model")
print(f"Model: {model_path}")
# Load tokenizer
self.tokenizer = AutoTokenizer.from_pretrained(
model_path,
trust_remote_code=True,
)
print(f"Tokenizer loaded: {len(self.tokenizer)} tokens")
# Validate emotion tags
self._validate_emotion_tags()
# Precompute special token strings
self._init_special_tokens()
# Initialize vLLM engine
print(f"Initializing vLLM engine...")
engine_args = AsyncEngineArgs(
model=model_path,
tokenizer=model_path,
dtype=dtype,
max_model_len=max_model_len,
gpu_memory_utilization=gpu_memory_utilization,
tensor_parallel_size=tensor_parallel_size,
trust_remote_code=True,
disable_log_stats=False,
**engine_kwargs
)
self.engine = AsyncLLMEngine.from_engine_args(engine_args)
print(f"Maya1 Model ready\n")
def _validate_emotion_tags(self):
"""Validate that all 20 emotion tags are single tokens."""
failed_tags = []
for tag in ALL_EMOTION_TAGS:
token_ids = self.tokenizer.encode(tag, add_special_tokens=False)
if len(token_ids) != 1:
failed_tags.append((tag, len(token_ids)))
if failed_tags:
print(f"ERROR: {len(failed_tags)} emotion tags are NOT single tokens!")
raise AssertionError(f"Emotion tags validation failed")
print(f"All {len(ALL_EMOTION_TAGS)} emotion tags validated")
def _init_special_tokens(self):
"""Precompute special token strings for fast prefix building."""
self.soh_token = self.tokenizer.decode([SOH_ID])
self.bos_token = self.tokenizer.bos_token
self.eot_token = self.tokenizer.decode([TEXT_EOT_ID])
self.eoh_token = self.tokenizer.decode([EOH_ID])
self.soa_token = self.tokenizer.decode([SOA_ID])
self.sos_token = self.tokenizer.decode([CODE_START_TOKEN_ID])
async def generate(self, prompt: str, sampling_params: SamplingParams):
"""
Generate tokens from prompt (non-streaming).
Args:
prompt: Input prompt
sampling_params: vLLM sampling parameters
Returns:
Generated output from vLLM
"""
request_id = f"req_{id(prompt)}"
# Collect results from async generator
final_output = None
async for output in self.engine.generate(
prompt=prompt,
sampling_params=sampling_params,
request_id=request_id
):
final_output = output
return [final_output] if final_output else []
async def generate_stream(self, prompt: str, sampling_params: SamplingParams):
"""
Generate tokens from prompt (streaming).
Args:
prompt: Input prompt
sampling_params: vLLM sampling parameters
Yields:
Generated outputs from vLLM
"""
request_id = f"req_{id(prompt)}"
# Stream from engine
async for output in self.engine.generate(
prompt=prompt,
sampling_params=sampling_params,
request_id=request_id
):
yield output
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