github.com Open in urlscan Pro
140.82.121.4  Public Scan

Submitted URL: http://ultravox.ai./
Effective URL: https://github.com/fixie-ai/ultravox
Submission: On June 05 via api from US — Scanned from DE

Form analysis 3 forms found in the DOM

GET

<form id="query-builder-test-form" action="" accept-charset="UTF-8" method="get">
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  </query-builder>
</form>

POST /search/feedback

<form id="code-search-feedback-form" data-turbo="false" action="/search/feedback" accept-charset="UTF-8" method="post"><input type="hidden" data-csrf="true" name="authenticity_token"
    value="MK32p7YftRdnL3FhxuFOV6rz29X2pDl3/rmJoaCvBjvTz9ri8LLjCjpMjg8QZHqmG7BuyW9vGYYcr7RVTmoSXA==">
  <p>We read every piece of feedback, and take your input very seriously.</p>
  <textarea name="feedback" class="form-control width-full mb-2" style="height: 120px" id="feedback"></textarea>
  <input name="include_email" id="include_email" aria-label="Include my email address so I can be contacted" class="form-control mr-2" type="checkbox">
  <label for="include_email" style="font-weight: normal">Include my email address so I can be contacted</label>
</form>

POST /search/custom_scopes

<form id="custom-scopes-dialog-form" data-turbo="false" action="/search/custom_scopes" accept-charset="UTF-8" method="post"><input type="hidden" data-csrf="true" name="authenticity_token"
    value="G4Zpp0zhSdcLc5iuleipFloZGYVAAW/opyyVTZ2ELHunHU2/M6SfbaAUxtvBhpPNlZVSGo3dJSh7gh2+l4Juxg==">
  <div data-target="custom-scopes.customScopesModalDialogFlash"></div>
  <input type="hidden" id="custom_scope_id" name="custom_scope_id" data-target="custom-scopes.customScopesIdField">
  <div class="form-group">
    <label for="custom_scope_name">Name</label>
    <auto-check src="/search/custom_scopes/check_name" required="">
      <input type="text" name="custom_scope_name" id="custom_scope_name" data-target="custom-scopes.customScopesNameField" class="form-control" autocomplete="off" placeholder="github-ruby" required="" maxlength="50" spellcheck="false">
      <input type="hidden" data-csrf="true" value="8e+bV8YdhdhER7oBKvhOTio/8e1uftrmQsUSL+PArbpjKGX1+Ux14PLnirD8ylDif6uQ/aq6HTGw0BFWaanFwg==">
    </auto-check>
  </div>
  <div class="form-group">
    <label for="custom_scope_query">Query</label>
    <input type="text" name="custom_scope_query" id="custom_scope_query" data-target="custom-scopes.customScopesQueryField" class="form-control" autocomplete="off" placeholder="(repo:mona/a OR repo:mona/b) AND lang:python" required=""
      maxlength="500">
  </div>
  <p class="text-small color-fg-muted"> To see all available qualifiers, see our <a class="Link--inTextBlock" href="https://docs.github.com/search-github/github-code-search/understanding-github-code-search-syntax">documentation</a>. </p>
</form>

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FIXIE-AI/ULTRAVOX

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Optionally include "passage" in BoolQ samples (#6)
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REPOSITORY FILES NAVIGATION

 * README
 * MIT license


AN OPEN, FAST, AND EXTENSIBLE MULTIMODAL LLM


ABOUT

Ultravox is a new kind of multimodal LLM that can understand text as well as
human speech, without the need for a separate Audio Speech Recognition (ASR)
stage. Building on research like AudioLM, SeamlessM4T, Gazelle, SpeechGPT, and
others, we've extended Meta's Llama 3 model with a multimodal projector that
converts audio directly into the high-dimensional space used by Llama 3. This
direct coupling allows Ultravox to respond much more quickly than systems that
combine separate ASR and LLM components. In the future this will also allow
Ultravox to natively understand the paralinguistic cues of timing and emotion
that are omnipresent in human speech.

The current version of Ultravox (v0.1), when invoked with audio content, has a
time-to-first-token (TTFT) of approximately 200ms, and a tokens-per-second rate
of ~100, all using a Llama 3 8B backbone. While quite fast, we believe there is
considerable room for improvement in these numbers.

Ultravox currently takes in audio and emits streaming text. As we evolve the
model, we'll train it to be able to emit a stream of speech tokens that can then
be converted directly into raw audio by an appropriate unit vocoder. We're
interested in working with interested parties to build this functionality!


DEMO

See Ultravox in action via a voice call with an AI in ai.town.


DISCORD

Join us on our Discord server here.


INFERENCE SERVER

You can try out Ultravox using your own audio content (as a WAV file), using the
following curl command:

curl -X POST -H "Authorization: Bearer $ULTRAVOX_API_KEY" -H "Content-Type: application/json" \
     -d @data.json https://ultravox.api.fixie.ai/v1/chat/completions



where data.json contains:

{ 
  "model": "fixie-ai/ultravox-v0.1",
  "messages": [{ 
    "role": "user",
    "content": [{
      "type": "text",
      "text": "What’s in <|audio|>?"
    }, {
      "type": "image_url",
      "image_url": {
        "url": "data:audio/wav;base64,{base64_wav}"
      }
    }]
  }],
  "stream": true
}




MODEL

You can download the latest weights from the Ultravox Hugging Face page.


ARCHITECTURE




CONTRIBUTING

Read on if you're interested in training your own version of Ultravox.


ENVIRONMENT SETUP (MAC)

Install the basic tools:

 * Homebrew is a package manager for MacOS that also mostly works for Linux. If
   you're running Debian or Ubuntu Linux, you can alternatively get by with apt.
 * Just simplifies our shell workflows. It frequently functions as our interface
   to all the other tools.

/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
brew update
brew install just



Create a Python virtual environment and install the necessary packages:

just create-env



For now we're using venv for Python virtual environments. We may switch to
Poetry in the future.


MOSAIC ENVIRONMENT SETUP

You need to setup a few things to run on the Mosaic Platform.

 1. Install & login to the Mosaic CLI

pip install --upgrade mosaicml-cli

mcli init

mcli set api-key <new-value>


 2. set API keys for tools we use:

# Huggging Face token for accessing walled data and models
mcli create secret env HF_TOKEN=hf_<your_token>

# WandB token for logging experiments
mcli create secret env WANDB_PROJECT=ultravox
mcli create secret env WANDB_API_KEY=<your_wandb_key>

# GCP credentials for accessing data (e.g. BoolQ)
# Get service_account.json file from Justin/Farzad and put it in the root dir, then
mcli create secret gcp




TRAINING

just train



For DDP training make sure to use: torchrun --nproc_per_node=8 -m
ultravox.training.train


LOCAL TRAINING

python -m ultravox.training.train --config_path ultravox/training/configs/asr_tinyllama.yaml  --data_set 'dummy' --device cpu --batch_size 1  --exp_name <give_your_experiment_a_name>




MOSAICML TRAINING

You need to setup your SSH key in the Mosaic Platform:
https://docs.mosaicml.com/projects/mcli/en/latest/resources/secrets/ssh.html#page-secrets-ssh

## Create a new SSH key and add it to the Mosaic Platform
# ssh-keygen -f ~/.ssh/mclid_id_rsa
## add the **public** key to Github
# mcli create secret ssh ~/.ssh/mclid_id_rsa

mcli run -f mcloud.yaml --follow



Other useful commands:

mcli get clusters

mcli util r7z2
mcli get runs
mcli get runs --cluster r7z2

mcli run -f mcloud.yaml --follow



For interactive runs, we don't recommend using --interactive. Instead set the
command to be something like sleep 3600 and then connect to it using mcli
connect <job_name> --tmux. This way your environment (code and packages) will be
the same as the training environment. The value 3600 (1 hour), is used as an
example.

IMPORTANT: Make sure to stop the machine when you're done with any job,
specially interactive ones!


RUNNING EVALUATIONS

 1. Use infer_tool.py --json > file to create a jsonl output from a given
    model/dataset combo, where each line contains two values: question and
    answer.
 2. Use eval_tool.py -f file to evaluate the jsonl file, which will produce an
    average score for the model on the dataset.


MISC

Useful commands:

just update    # update dependencies
just format    # run formatting (black, isort, autoflake)
just python    # activate venv and run python
just pip       # install a package in the venv using the right pip




ABOUT

No description, website, or topics provided.


RESOURCES

Readme


LICENSE

MIT license
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STARS

46 stars


WATCHERS

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FORKS

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PACKAGES 0

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CONTRIBUTORS 2

 * juberti Justin Uberti
 * farzadab Farzad Abdolhosseini


LANGUAGES


 * Python 97.5%
 * Just 1.6%
 * Shell 0.9%


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