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import logging
from typing import Any, Dict, List, Optional
import numpy as np
import transformers
# We must use relative import in this directory to allow uploading to HF Hub
# Even "from . import X" pattern doesn't work (undocumented and unclear why)
from .ultravox_model import UltravoxModel
from .ultravox_processing import UltravoxProcessor
class UltravoxPipeline(transformers.Pipeline):
def __init__(
self,
model: UltravoxModel,
tokenizer: Optional[transformers.PreTrainedTokenizerBase] = None,
audio_processor: Optional[transformers.ProcessorMixin] = None,
**kwargs
):
if tokenizer is None:
try:
tokenizer = transformers.AutoTokenizer.from_pretrained(
model.config._name_or_path
)
except:
tokenizer = transformers.AutoTokenizer.from_pretrained(
model.config.text_model_id or model.config.text_config._name_or_path
)
if audio_processor is None:
audio_processor = transformers.AutoProcessor.from_pretrained(
model.config.audio_model_id or model.config.audio_config._name_or_path
)
self.processor = UltravoxProcessor(
audio_processor=audio_processor,
tokenizer=tokenizer,
stack_factor=model.config.stack_factor,
)
super().__init__(model=model, tokenizer=tokenizer, **kwargs)
def _sanitize_parameters(self, **kwargs):
generation_keys = ["temperature", "max_new_tokens", "repetition_penalty"]
generation_kwargs = {k: kwargs[k] for k in kwargs if k in generation_keys}
return {}, generation_kwargs, {}
def preprocess(self, inputs: Dict[str, Any]):
turns: list = inputs.get("turns", [])
audio = inputs.get("audio", None)
# Convert to float32 if needed.
if isinstance(audio, np.ndarray):
if audio.dtype == np.float64:
audio = audio.astype(np.float32)
elif audio.dtype == np.int16:
audio = audio.astype(np.float32) / np.float32(32768.0)
elif audio.dtype == np.int32:
audio = audio.astype(np.float32) / np.float32(2147483648.0)
if audio is not None and (len(turns) == 0 or turns[-1]["role"] != "user"):
prompt = inputs.get("prompt", "<|audio|>")
if "<|audio|>" not in prompt:
logging.warning(
"Prompt does not contain '<|audio|>', appending '<|audio|>' to the end of the prompt."
)
prompt += " <|audio|>"
turns.append({"role": "user", "content": prompt})
text = self.processor.tokenizer.apply_chat_template(
turns, add_generation_prompt=True, tokenize=False
)
if "sampling_rate" not in inputs and audio is not None:
logging.warning(
"No sampling rate provided, using default of 16kHz. We highly recommend providing the correct sampling rate."
)
output = self.processor(
text=text,
audio=audio,
sampling_rate=inputs.get("sampling_rate", 16000),
)
if "audio_values" in output:
output["audio_values"] = output["audio_values"].to(self.model.dtype)
return output
def _forward(
self,
model_inputs: Dict[str, Any],
temperature: Optional[float] = None,
max_new_tokens: Optional[int] = None,
repetition_penalty: float = 1.1,
) -> List[int]:
temperature = temperature or None
do_sample = temperature is not None
terminators = [self.tokenizer.eos_token_id]
if "<|eot_id|>" in self.tokenizer.added_tokens_encoder:
terminators.append(self.tokenizer.convert_tokens_to_ids("<|eot_id|>"))
input_len = model_inputs["input_ids"].shape[1]
outputs = self.model.generate(
**model_inputs,
do_sample=do_sample,
temperature=temperature,
max_new_tokens=max_new_tokens,
repetition_penalty=repetition_penalty,
eos_token_id=terminators
)
return outputs[0][input_len:]
def postprocess(self, model_outputs) -> str:
output_text = self.tokenizer.decode(model_outputs, skip_special_tokens=True)
return output_text
transformers.pipelines.PIPELINE_REGISTRY.register_pipeline(
"ultravox-pipeline",
pipeline_class=UltravoxPipeline,
pt_model=transformers.AutoModel,
type="multimodal",
)
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