Upload ultravox_pipeline.py with huggingface_hub

ultravox_pipeline.py

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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
+            )
+
+        super().__init__(model=model, tokenizer=tokenizer, **kwargs)
+
+        self.processor = UltravoxProcessor(
+            audio_processor=audio_processor,
+            tokenizer=tokenizer,
+            stack_factor=model.config.stack_factor,
+            audio_context_size=model.audio_tower_context_length,
+        )
+
+    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",
+)