Tasks and inputs
facebook/mms-tts-bod is listed for Text-to-speech. It accepts text and produces audio. Use the publisher’s input format and preprocessing for your chosen deployment.
SyncDev model directory · Profile updated Oct 7, 2026
Hugging Face repository for Text-to-speech.
The specification score summarizes recorded features and limits. It is not a benchmark result.
Recorded modality support shows what this model can accept and produce. An undocumented modality is shown as unknown rather than assumed to be unsupported.
Accepts
Produces
facebook/mms-tts-bod is a Hugging Face repository returned by the public Text-to-speech task feed. Its recorded download count is 136,297 and it has 10 likes when the repository snapshot was collected. Popularity is useful for discovery, but it is not a performance ranking or a guarantee of suitability.
This record is categorized for Text-to-speech. A task category describes the intended machine-learning problem; it does not prove that every version, checkpoint, quantization, or deployment method produces the same quality. Teams should read the model card and test representative inputs before choosing it.
The public repository lists transformers as its library metadata. Its recorded tags include transformers, pytorch, safetensors, vits, text-to-audio, mms, text-to-speech, arxiv:2305.13516. These source details are retained so readers can trace this directory entry back to the repository instead of relying on copied descriptions.
Test names, numbers, abbreviations, punctuation and the languages your audience uses. Listen for pronunciation, pacing and artifacts; measure startup latency and confirm voice licensing.
Explore facebook models and the text-to-speech collection. Use the comparison tool to inspect a second candidate.
Compare the model on the work you need to complete.
facebook/mms-tts-bod is listed for Text-to-speech. It accepts text and produces audio. Use the publisher’s input format and preprocessing for your chosen deployment.
Compare pronunciation, voice consistency and time to the first playable audio. Check the service’s billing unit for the actual script.
Use the model identifier and account requirements for the selected API route. Confirm its request limits and output format with one complete task before connecting a larger workflow.
These records share a directory category. Check the source card, output type and deployment requirements before treating a model as a replacement.
Gemini 3.8 Flash-Lite TTS converts supplied text into speech through the Gemini API. It is a candidate for read-aloud features and repeated single-speaker audio jobs.
Gemini 3.8 Flash TTS turns a supplied text transcript into audio. It is a speech-generation model for narration and dialogue, with speaker and style information supplied separately from the words to be spoken.
xAI
Grok Voice TTS 1.0 from xAI: Convert text into spoken audio with a single API call. The API supports a rich set of expressive voices, inline speech tags for fine-grained delivery control, and output formats from high-fidelity MP3 to telephony-optimized μ-law.
OpenAI
GPT-Realtime-2.1 from OpenAI: Realtime speech-to-speech model with configurable reasoning, tool use, and robust voice-agent behavior.
Gemini 3.5 Live Translate Preview from Google: Low-latency audio-to-audio model for real-time speech translation across 70+ languages.
Gemini 3.1 Flash TTS Preview from Google: Low-latency speech generation with steerable prompts and expressive audio tags.