Add transcriber tool and PipelineTool

docs/source/conceptual_guides/intro_agents.md

@@ -108,4 +108,8 @@ Code is just a better way to express actions on a computer. It has better:
 - **Generality:** code is built to express simply anything you can do have a computer do.
 - **Representation in LLM training corpuses:** why not leverage this benediction of the sky that plenty of quality actions have already been included in LLM training corpuses?
 
-This is why we put emphasis on proposing code agents, in this case python agents, which meant putting higher effort on building python interpreters.
+This is illustrated on the figure below, taken from [Executable Code Actions Elicit Better LLM Agents](https://huggingface.co/papers/2402.01030).
+
+<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/code_vs_json_actions.png">
+
+This is why we put emphasis on proposing code agents, in this case python agents, which meant putting higher effort on building secure python interpreters.

docs/source/guided_tour.md

@@ -17,18 +17,16 @@ rendered properly in your Markdown viewer.
 
 [[open-in-colab]]
 
-This visit of your framework should take about 15 minutes. It will show you how to build an agent, how to run it, and how to customize it to make it work better for your use-case. For more in-depth usage, you will then want to check out our tutorials like [Building good agents](./tutorials/building_good_agents).
+In this guided visit, you will learn how to build an agent, how to run it, and how to customize it to make it work better for your use-case.
 
 ### Building your agent
 
-To initialize an agent, you need these arguments:
+To initialize a minimal agent, you need at least these two arguments:
 
 - An LLM to power your agent - because the agent is different from a simple LLM, it is a system that uses a LLM as its engine.
-- A toolbox from which the agent pick tools to execute
+- A list of tools from which the agent pick tools to execute
 
-Upon initialization of the agent system, a system prompt (attribute `system_prompt`) is built automatically by turning the description extracted from the tools into a predefined system prompt template. But you can customize it!
+For defining your llm, you can make a `llm_engine` method which accepts a list of [messages](./chat_templating) and returns text. This callable also needs to accept a `stop_sequences` argument that indicates when to stop generating.
-
-For defining your llm, you can make a `llm_engine` method which accepts a list of [messages](./chat_templating) and returns text. This callable also needs to accept a `stop` argument that indicates when to stop generating.
 
 ```python
 from huggingface_hub import login, InferenceClient
@@ -51,10 +49,14 @@ You could use any `llm_engine` method as long as:
 
 Additionally, `llm_engine` can also take a `grammar` argument. In the case where you specify a `grammar` upon agent initialization, this argument will be passed to the calls to llm_engine, with the `grammar` that you defined upon initialization, to allow [constrained generation](https://huggingface.co/docs/text-generation-inference/conceptual/guidance) in order to force properly-formatted agent outputs.
 
+For convenience, we provide pre-built classes for your llm engine:
+- [`TransformersEngine`] takes a pre-initialized `transformers` pipeline to run inference on your local machine using `transformers`.
+- [`HfApiEngine`] leverages a `huggingface_hub.InferenceClient` under the hood.
+- We also provide [`OpenAIEngine`] and [`AnthropicEngine`] but you could use anything!
+
 You will also need a `tools` argument which accepts a list of `Tools` - it can be an empty list. You can also add the default toolbox on top of your `tools` list by defining the optional argument `add_base_tools=True`.
 
-Now you can create an agent, like [`CodeAgent`], and run it. You can also create a [`TransformersEngine`] with a pre-initialized pipeline to run inference on your local machine using `transformers`.
+Once you have these two arguments, `tools` and `llm_engine`,  you can create an agent and run it. 
-For convenience, since agentic behaviours generally require strong models that are harder to run locally for now, we also provide the [`HfApiEngine`] class that initializes a `huggingface_hub.InferenceClient` under the hood. 
 
 ```python
 from agents import CodeAgent, HfApiEngine
@@ -63,12 +65,10 @@ llm_engine = HfApiEngine(model=model_id)
 agent = CodeAgent(tools=[], llm_engine=llm_engine, add_base_tools=True)
 
 agent.run(
-    "Could you translate this sentence from French, say it out loud and return the audio.",
+    "Could you give me the 118th number in the Fibonacci sequence?",
-    sentence="Où est la boulangerie la plus proche?",
 )
 ```
 
-This will be handy in case of emergency baguette need!
 You can even leave the argument `llm_engine` undefined, and an [`HfApiEngine`] will be created by default.
 
 ```python
@@ -77,24 +77,23 @@ from agents import CodeAgent
 agent = CodeAgent(tools=[], add_base_tools=True)
 
 agent.run(
-    "Could you translate this sentence from French, say it out loud and give me the audio.",
+    "Could you give me the 118th number in the Fibonacci sequence?",
-    sentence="Où est la boulangerie la plus proche?",
+    additional_detail="We adopt the convention where the first two numbers are 0 and 1."
 )
 ```
 
-Note that we used an additional `sentence` argument: you can pass text as additional arguments to the model.
+Note that we used an additional `additional_detail` argument: you can additional kwargs to `agent.run()`, they will be baked into the prompt as text.
 
-You can also use this to indicate the path to local or remote files for the model to use:
+You can use this to indicate the path to local or remote files for the model to use:
 
 ```py
-from agents import CodeAgent
+from agents import CodeAgent, Tool, SpeechToTextTool
 
-agent = CodeAgent(tools=[], llm_engine=llm_engine, add_base_tools=True)
+agent = CodeAgent(tools=[SpeechToTextTool()], add_base_tools=True)
 
 agent.run("Why does Mike not know many people in New York?", audio="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/recording.mp3")
 ```
 
-
 It's important to explain as clearly as possible the task you want to perform.
 Since an agent is powered by an LLM, minor variations in your task formulation might yield completely different results.
 You can also run an agent consecutively for different tasks: if you leave the default option of `True` for the flag `reset` when calling `agent.run(task)`, the agent's memory will be erased before starting the new task.
@@ -117,14 +116,18 @@ This gives you at the end of the agent run:
 ```text
 'Hugging Face – Blog'
 ```
-The execution will stop at any code trying to perform an illegal operation or if there is a regular Python error with the code generated by the agent.
+The execution will stop at any code trying to perform an illegal operation or if there is a regular Python error with the code generated by the agent. You can also use E2B code executor instead of a local Python interpreter by passing `use_e2b_executor=True` upon agent initialization.
 
 > [!WARNING]
 > The LLM can generate arbitrary code that will then be executed: do not add any unsafe imports!
 
 ### The system prompt
 
-An agent, or rather the LLM that drives the agent, generates an output based on the system prompt. The system prompt can be customized and tailored to the intended task. For example, check the system prompt for the [`CodeAgent`] (below version is slightly simplified).
+Upon initialization of the agent system, a system prompt (attribute `system_prompt`) is built automatically by turning the description extracted from the tools into a predefined system prompt template.
+
+But you can customize it!
+
+Let's see how it works. For example, check the system prompt for the [`CodeAgent`] (below version is slightly simplified).
 
 The prompt and output parser were automatically defined, but you can easily inspect them by calling the `system_prompt_template` on your agent.
 
@@ -207,6 +210,7 @@ Transformers comes with a default toolbox for empowering agents, that you can ad
 
 - **DuckDuckGo web search***: performs a web search using DuckDuckGo browser.
 - **Python code interpreter**: runs your the LLM generated Python code in a secure environment. This tool will only be added to [`JsonAgent`] if you initialize it with `add_base_tools=True`, since code-based agent can already natively execute Python code
+- **Transcriber**: a speech-to-text pipeline built on Whisper-Turbo that transcribes an audio to text.
 
 You can manually use a tool by calling the [`load_tool`] function and a task to perform.
 
@@ -217,7 +221,6 @@ search_tool = load_tool("web_search")
 print(search_tool("Who's the current president of Russia?"))
 ```
 
-
 ### Create a new tool
 
 You can create your own tool for use cases not covered by the default tools from Hugging Face.
@@ -320,7 +323,7 @@ manager_agent.run("Who is the CEO of Hugging Face?")
 > For an in-depth example of an efficient multi-agent implementation, see [how we pushed our multi-agent system to the top of the GAIA leaderboard](https://huggingface.co/blog/beating-gaia).
 
 
-## Talk with your agent in a cool Gradio interface
+## Talk with your agent and visualize its thoughts in a cool Gradio interface
 
 You can use `GradioUI` to interactively submit tasks to your agent and observe its thought and execution process, here is an example:
 
@@ -345,3 +348,10 @@ GradioUI(agent).launch()
 
 Under the hood, when the user types a new answer, the agent is launched with `agent.run(user_request, reset=False)`.
 The `reset=False` flag means the agent's memory is not flushed before launching this new task, which lets the conversation go on.
+
+## Next steps
+
+For more in-depth usage, you will then want to check out our tutorials:
+- [the explanation of how our code agents work](./tutorials/secure_code_execution)
+- [this guide on how to build good agents](./tutorials/building_good_agents).
+- [the in-depth guide for tool usage](./tutorials/building_good_agents).

docs/source/tutorials/secure_code_execution.md

@@ -29,6 +29,10 @@ Code is just a better way to express actions on a computer. It has better:
 - **Generality:** code is built to express simply anything you can do have a computer do.
 - **Representation in LLM training corpuses:** why not leverage this benediction of the sky that plenty of quality actions have already been included in LLM training corpuses?
 
+This is illustrated on the figure below, taken from [Executable Code Actions Elicit Better LLM Agents](https://huggingface.co/papers/2402.01030).
+
+<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/code_vs_json_actions.png">
+
 This is why we put emphasis on proposing code agents, in this case python agents, which meant putting higher effort on building secure python interpreters.
 
 ### Local python interpreter

docs/source/tutorials/tools.md

@@ -91,7 +91,7 @@ model_download_tool = load_tool(
 )
 ```
 
-### Import a Space as a tool 🚀
+### Import a Space as a tool
 
 You can directly import a Space from the Hub as a tool using the [`Tool.from_space`] method!
 
@@ -103,7 +103,8 @@ For instance, let's import the [FLUX.1-dev](https://huggingface.co/black-forest-
 image_generation_tool = Tool.from_space(
     "black-forest-labs/FLUX.1-schnell",
     name="image_generator",
-    description="Generate an image from a prompt")
+    description="Generate an image from a prompt"
+)
 
 image_generation_tool("A sunny beach")
 ```

examples/e2b_example.py

@@ -1,9 +1,8 @@
 from agents import Tool, CodeAgent
 from agents.default_tools.search import VisitWebpageTool
 from dotenv import load_dotenv
-load_dotenv()
 
-LAUNCH_GRADIO = False
+load_dotenv()
 
 class GetCatImageTool(Tool):
     name="get_cat_image"
@@ -24,6 +23,8 @@ class GetCatImageTool(Tool):
 
         return Image.open(BytesIO(response.content))
 
+LAUNCH_GRADIO = False
+
 get_cat_image = GetCatImageTool()
 
 

src/agents/__init__.py

@@ -24,17 +24,16 @@ from transformers.utils.import_utils import define_import_structure
 
 if TYPE_CHECKING:
     from .agents import *
-    from .default_tools.base import *
+    from .default_tools import *
-    from .default_tools.search import *
     from .gradio_ui import *
     from .llm_engines import *
     from .local_python_executor import *
+    from .e2b_executor import *
     from .monitoring import *
     from .prompts import *
     from .tools import *
     from .types import *
     from .utils import *
-    from .default_tools.search import *
 
 
 else:

src/agents/agents.py

@@ -25,7 +25,7 @@ from rich.text import Text
 
 from .utils import console, parse_code_blob, parse_json_tool_call, truncate_content
 from .types import AgentAudio, AgentImage
-from .default_tools.base import FinalAnswerTool
+from .default_tools import FinalAnswerTool
 from .llm_engines import HfApiEngine, MessageRole
 from .monitoring import Monitor
 from .prompts import (

src/agents/default_tools/base.py

@@ -1,136 +0,0 @@
-#!/usr/bin/env python
-# coding=utf-8
-
-# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-import json
-from dataclasses import dataclass
-from typing import Dict
-
-from huggingface_hub import hf_hub_download, list_spaces
-
-from transformers.utils import is_offline_mode
-from ..local_python_executor import (
-    BASE_BUILTIN_MODULES,
-    BASE_PYTHON_TOOLS,
-    evaluate_python_code,
-)
-from ..tools import TOOL_CONFIG_FILE, Tool
-
-
-@dataclass
-class PreTool:
-    name: str
-    inputs: Dict[str, str]
-    output_type: type
-    task: str
-    description: str
-    repo_id: str
-
-
-def get_remote_tools(logger, organization="huggingface-tools"):
-    if is_offline_mode():
-        logger.info("You are in offline mode, so remote tools are not available.")
-        return {}
-
-    spaces = list_spaces(author=organization)
-    tools = {}
-    for space_info in spaces:
-        repo_id = space_info.id
-        resolved_config_file = hf_hub_download(
-            repo_id, TOOL_CONFIG_FILE, repo_type="space"
-        )
-        with open(resolved_config_file, encoding="utf-8") as reader:
-            config = json.load(reader)
-        task = repo_id.split("/")[-1]
-        tools[config["name"]] = PreTool(
-            task=task,
-            description=config["description"],
-            repo_id=repo_id,
-            name=task,
-            inputs=config["inputs"],
-            output_type=config["output_type"],
-        )
-
-    return tools
-
-
-class PythonInterpreterTool(Tool):
-    name = "python_interpreter"
-    description = "This is a tool that evaluates python code. It can be used to perform calculations."
-    inputs = {
-        "code": {
-            "type": "string",
-            "description": "The python code to run in interpreter",
-        }
-    }
-    output_type = "string"
-
-    def __init__(self, *args, authorized_imports=None, **kwargs):
-        if authorized_imports is None:
-            self.authorized_imports = list(set(BASE_BUILTIN_MODULES))
-        else:
-            self.authorized_imports = list(
-                set(BASE_BUILTIN_MODULES) | set(authorized_imports)
-            )
-        self.inputs = {
-            "code": {
-                "type": "string",
-                "description": (
-                    "The code snippet to evaluate. All variables used in this snippet must be defined in this same snippet, "
-                    f"else you will get an error. This code can only import the following python libraries: {authorized_imports}."
-                ),
-            }
-        }
-        self.base_python_tool = BASE_PYTHON_TOOLS
-        self.python_evaluator = evaluate_python_code
-        super().__init__(*args, **kwargs)
-
-    def forward(self, code: str) -> str:
-        output = str(
-            self.python_evaluator(
-                code,
-                static_tools=self.base_python_tool,
-                authorized_imports=self.authorized_imports,
-            )
-        )
-        return output
-
-
-class FinalAnswerTool(Tool):
-    name = "final_answer"
-    description = "Provides a final answer to the given problem."
-    inputs = {
-        "answer": {"type": "object", "description": "The final answer to the problem"}
-    }
-    output_type = "object"
-
-    def forward(self, answer):
-        return answer
-
-
-class UserInputTool(Tool):
-    name = "user_input"
-    description = "Asks for user's input on a specific question"
-    inputs = {
-        "question": {"type": "string", "description": "The question to ask the user"}
-    }
-    output_type = "string"
-
-    def forward(self, question):
-        user_input = input(f"{question} => ")
-        return user_input
-
-
-__all__ = ["PythonInterpreterTool", "FinalAnswerTool", "UserInputTool"]

src/agents/default_tools/search.py

@@ -1,81 +0,0 @@
-#!/usr/bin/env python
-# coding=utf-8
-
-# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-import re
-
-from ..tools import Tool
-
-
-class DuckDuckGoSearchTool(Tool):
-    name = "web_search"
-    description = """Performs a web search based on your query (think a Google search) then returns the top search results as a list of dict elements.
-    Each result has keys 'title', 'href' and 'body'."""
-    inputs = {
-        "query": {"type": "string", "description": "The search query to perform."}
-    }
-    output_type = "any"
-
-    def forward(self, query: str) -> str:
-        try:
-            from duckduckgo_search import DDGS
-        except ImportError:
-            raise ImportError(
-                "You must install package `duckduckgo_search` to run this tool: for instance run `pip install duckduckgo-search`."
-            )
-        results = DDGS().text(query, max_results=7)
-        return results
-
-
-class VisitWebpageTool(Tool):
-    name = "visit_webpage"
-    description = "Visits a webpage at the given url and returns its content as a markdown string."
-    inputs = {
-        "url": {
-            "type": "string",
-            "description": "The url of the webpage to visit.",
-        }
-    }
-    output_type = "string"
-
-    def forward(self, url: str) -> str:
-        try:
-            from markdownify import markdownify
-            import requests
-            from requests.exceptions import RequestException
-        except ImportError:
-            raise ImportError(
-                "You must install packages `markdownify` and `requests` to run this tool: for instance run `pip install markdownify requests`."
-            )
-        try:
-            # Send a GET request to the URL
-            response = requests.get(url)
-            response.raise_for_status()  # Raise an exception for bad status codes
-
-            # Convert the HTML content to Markdown
-            markdown_content = markdownify(response.text).strip()
-
-            # Remove multiple line breaks
-            markdown_content = re.sub(r"\n{3,}", "\n\n", markdown_content)
-
-            return markdown_content
-
-        except RequestException as e:
-            return f"Error fetching the webpage: {str(e)}"
-        except Exception as e:
-            return f"An unexpected error occurred: {str(e)}"
-
-
-__all__ = ["DuckDuckGoSearchTool", "VisitWebpageTool"]

src/agents/llm_engines.py

@@ -237,20 +237,20 @@ class HfApiEngine(HfEngine):
 
         # Send messages to the Hugging Face Inference API
         if grammar is not None:
-            response = self.client.chat_completion(
+            output = self.client.chat_completion(
                 messages,
                 stop=stop_sequences,
                 response_format=grammar,
                 max_tokens=max_tokens,
             )
         else:
-            response = self.client.chat.completions.create(
+            output = self.client.chat.completions.create(
                 messages, stop=stop_sequences, max_tokens=max_tokens
             )
 
-        response = response.choices[0].message.content
+        response = output.choices[0].message.content
-        self.last_input_token_count = response.usage.prompt_tokens
+        self.last_input_token_count = output.usage.prompt_tokens
-        self.last_output_token_count = response.usage.completion_tokens
+        self.last_output_token_count = output.usage.completion_tokens
         return response
 
     def get_tool_call(

src/agents/tools.py

@@ -753,6 +753,7 @@ def launch_gradio_demo(tool: Tool):
 TOOL_MAPPING = {
     "python_interpreter": "PythonInterpreterTool",
     "web_search": "DuckDuckGoSearchTool",
+    "transcriber": "SpeechToTextTool"
 }
 
 
@@ -1003,6 +1004,160 @@ class Toolbox:
             toolbox_description += f"\t{tool.name}: {tool.description}\n"
         return toolbox_description
 
+from transformers import AutoProcessor
+from .types import handle_agent_input_types, handle_agent_output_types
+
+class PipelineTool(Tool):
+    """
+    A [`Tool`] tailored towards Transformer models. On top of the class attributes of the base class [`Tool`], you will
+    need to specify:
+
+    - **model_class** (`type`) -- The class to use to load the model in this tool.
+    - **default_checkpoint** (`str`) -- The default checkpoint that should be used when the user doesn't specify one.
+    - **pre_processor_class** (`type`, *optional*, defaults to [`AutoProcessor`]) -- The class to use to load the
+      pre-processor
+    - **post_processor_class** (`type`, *optional*, defaults to [`AutoProcessor`]) -- The class to use to load the
+      post-processor (when different from the pre-processor).
+
+    Args:
+        model (`str` or [`PreTrainedModel`], *optional*):
+            The name of the checkpoint to use for the model, or the instantiated model. If unset, will default to the
+            value of the class attribute `default_checkpoint`.
+        pre_processor (`str` or `Any`, *optional*):
+            The name of the checkpoint to use for the pre-processor, or the instantiated pre-processor (can be a
+            tokenizer, an image processor, a feature extractor or a processor). Will default to the value of `model` if
+            unset.
+        post_processor (`str` or `Any`, *optional*):
+            The name of the checkpoint to use for the post-processor, or the instantiated pre-processor (can be a
+            tokenizer, an image processor, a feature extractor or a processor). Will default to the `pre_processor` if
+            unset.
+        device (`int`, `str` or `torch.device`, *optional*):
+            The device on which to execute the model. Will default to any accelerator available (GPU, MPS etc...), the
+            CPU otherwise.
+        device_map (`str` or `dict`, *optional*):
+            If passed along, will be used to instantiate the model.
+        model_kwargs (`dict`, *optional*):
+            Any keyword argument to send to the model instantiation.
+        token (`str`, *optional*):
+            The token to use as HTTP bearer authorization for remote files. If unset, will use the token generated when
+            running `huggingface-cli login` (stored in `~/.huggingface`).
+        hub_kwargs (additional keyword arguments, *optional*):
+            Any additional keyword argument to send to the methods that will load the data from the Hub.
+    """
+
+    pre_processor_class = AutoProcessor
+    model_class = None
+    post_processor_class = AutoProcessor
+    default_checkpoint = None
+    description = "This is a pipeline tool"
+    name = "pipeline"
+    inputs = {"prompt": str}
+    output_type = str
+
+    def __init__(
+        self,
+        model=None,
+        pre_processor=None,
+        post_processor=None,
+        device=None,
+        device_map=None,
+        model_kwargs=None,
+        token=None,
+        **hub_kwargs,
+    ):
+        if not is_torch_available():
+            raise ImportError("Please install torch in order to use this tool.")
+
+        if not is_accelerate_available():
+            raise ImportError("Please install accelerate in order to use this tool.")
+
+        if model is None:
+            if self.default_checkpoint is None:
+                raise ValueError("This tool does not implement a default checkpoint, you need to pass one.")
+            model = self.default_checkpoint
+        if pre_processor is None:
+            pre_processor = model
+
+        self.model = model
+        self.pre_processor = pre_processor
+        self.post_processor = post_processor
+        self.device = device
+        self.device_map = device_map
+        self.model_kwargs = {} if model_kwargs is None else model_kwargs
+        if device_map is not None:
+            self.model_kwargs["device_map"] = device_map
+        self.hub_kwargs = hub_kwargs
+        self.hub_kwargs["token"] = token
+
+        super().__init__()
+
+    def setup(self):
+        """
+        Instantiates the `pre_processor`, `model` and `post_processor` if necessary.
+        """
+        from accelerate import PartialState
+
+        if isinstance(self.pre_processor, str):
+            self.pre_processor = self.pre_processor_class.from_pretrained(self.pre_processor, **self.hub_kwargs)
+
+        if isinstance(self.model, str):
+            self.model = self.model_class.from_pretrained(self.model, **self.model_kwargs, **self.hub_kwargs)
+
+        if self.post_processor is None:
+            self.post_processor = self.pre_processor
+        elif isinstance(self.post_processor, str):
+            self.post_processor = self.post_processor_class.from_pretrained(self.post_processor, **self.hub_kwargs)
+
+        if self.device is None:
+            if self.device_map is not None:
+                self.device = list(self.model.hf_device_map.values())[0]
+            else:
+                self.device = PartialState().default_device
+
+        if self.device_map is None:
+            self.model.to(self.device)
+
+        super().setup()
+
+    def encode(self, raw_inputs):
+        """
+        Uses the `pre_processor` to prepare the inputs for the `model`.
+        """
+        return self.pre_processor(raw_inputs)
+
+    def forward(self, inputs):
+        """
+        Sends the inputs through the `model`.
+        """
+        with torch.no_grad():
+            return self.model(**inputs)
+
+    def decode(self, outputs):
+        """
+        Uses the `post_processor` to decode the model output.
+        """
+        return self.post_processor(outputs)
+
+    def __call__(self, *args, **kwargs):
+        args, kwargs = handle_agent_input_types(*args, **kwargs)
+
+        if not self.is_initialized:
+            self.setup()
+
+        encoded_inputs = self.encode(*args, **kwargs)
+
+        import torch
+        from accelerate.utils import send_to_device
+
+        tensor_inputs = {k: v for k, v in encoded_inputs.items() if isinstance(v, torch.Tensor)}
+        non_tensor_inputs = {k: v for k, v in encoded_inputs.items() if not isinstance(v, torch.Tensor)}
+
+        encoded_inputs = send_to_device(tensor_inputs, self.device)
+        outputs = self.forward({**encoded_inputs, **non_tensor_inputs})
+        outputs = send_to_device(outputs, "cpu")
+        decoded_outputs = self.decode(outputs)
+
+        return handle_agent_output_types(decoded_outputs, self.output_type)
 
 __all__ = [
     "AUTHORIZED_TYPES",

src/agents/types.py

@@ -17,7 +17,7 @@ import pathlib
 import tempfile
 import uuid
 from io import BytesIO
-
+import requests
 import numpy as np
 
 from transformers.utils import (
@@ -224,7 +224,12 @@ class AgentAudio(AgentType, str):
             return self._tensor
 
         if self._path is not None:
-            tensor, self.samplerate = sf.read(self._path)
+            if "://" in str(self._path):
+                response = requests.get(self._path)
+                response.raise_for_status()
+                tensor, self.samplerate = sf.read(BytesIO(response.content))
+            else:
+                tensor, self.samplerate = sf.read(self._path)
             self._tensor = torch.tensor(tensor)
             return self._tensor