Best Audio Language Models to Run Locally in 2026

Quick Answer

OpenMOSS-Team’s MOSS-Transcribe-Diarize is the top pick as of September 2026 because it is the newest release among eligible candidates; this ranking admits only audio text to text models (Hugging Face pipeline tag: audio-text-to-text), with no public benchmark yet for any candidate, so the ordering is newest release first, then downloads. [7][9][3][10][5][1]

The ranking is: 1. In order, the ranking is MOSS-Transcribe-Diarize (OpenMOSS-Team), MOSS-Audio-4B-Instruct (OpenMOSS-Team), VibeVoice-ASR (Microsoft), ACE-Step Transcriber (ACE-Step), Music Flamingo (NVIDIA), and Audio Flamingo 3 (NVIDIA) [7][9].

Key Takeaways

• Ranking note: only audio text to text models with the Hugging Face pipeline tag audio-text-to-text qualify. All entries have no public benchmark yet; ordering is newest release first, then downloads.[7][9][3][10][5][1]
• OpenMOSS-Team’s MOSS-Transcribe-Diarize ranks first because its Hugging Face publication date, 2026-05-19, is the newest among the candidates.[7][9][3][10][5][1] OpenMOSS-Team’s MOSS-Audio-4B-Instruct follows, published 2026-04-10; its 5.2B parameters imply 2.6 GB of weight memory at 4-bit, estimated (params x 0.5 bytes).[9] Both use Apache-2.0.[7][9]
• Microsoft’s VibeVoice-ASR-HF combines 128K-token context with an MIT license.[3] Its 8.3B parameters imply 4.15 GB of weight memory at 4-bit, estimated (params x 0.5 bytes).[3]
• ACE-Step’s acestep-transcriber uses an MIT license; its 10.7B parameters imply 5.35 GB of weight memory at 4-bit, estimated (params x 0.5 bytes).[10]
• NVIDIA’s music-flamingo-2601-hf has 1,200-token context and 8.3B parameters, implying 4.15 GB of weight memory at 4-bit, estimated (params x 0.5 bytes).[5] Verify the license terms separately before deployment.
• NVIDIA’s audio-flamingo-3-hf has 32K-token context and 8.3B parameters, implying 4.15 GB of weight memory at 4-bit, estimated (params x 0.5 bytes).[1] Verify the license terms separately before deployment.

How do local audio language models compare on specifications and benchmarks?

Which audio language models should you consider running locally?

1. MOSS-Transcribe-Diarize

MOSS-Transcribe-Diarize by OpenMOSS-Team ranks first under the release-date ordering, with a Hugging Face publication date of May 19, 2026.[7] Ranking note: the ordering is newest release first, then downloads; this ranking admits only audio text to text models with the Hugging Face pipeline tag audio-text-to-text.[7][9][3][10][5][1] The benchmark status is “no public benchmark yet,” so its position should not be read as a demonstrated accuracy advantage.

The model has 909M parameters, a 128K-token context length, and 1.8 GB of BF16 weights.[7] Its weight-only memory at 4-bit is approximately 0.455 GB, estimated (params x 0.5 bytes) from the cited 909M parameters.[7] For local hardware planning, treat that calculation as a starting budget for weights. Choose a GPU only after checking the intended runtime’s total memory requirements; the weight estimate alone cannot establish whether a particular device will fit the model.

Use MOSS-Transcribe-Diarize for transcription with speaker diarization: producing a transcript that distinguishes speakers, the task identified in its accompanying paper.[8] The Apache-2.0 license is another concrete deployment detail.[7] The caveat is validation: with no public benchmark yet, evaluate transcription accuracy and speaker assignments on representative recordings before committing it to a local workflow.

2. MOSS-Audio-4B-Instruct

MOSS-Audio-4B-Instruct by OpenMOSS-Team ranks second because its Hugging Face publication date follows MOSS-Transcribe-Diarize by OpenMOSS-Team: April 10, 2026, versus May 19, 2026.[9][7] The ordering basis is newest release first, then downloads; no public benchmark yet establishes a performance order among the candidates. The ranking admits only audio text to text models with the Hugging Face pipeline tag audio-text-to-text. Its position therefore reflects release recency rather than demonstrated superiority.

The model has 5.2 billion parameters and 10.4 GB of BF16 weights.[9] Four-bit weight memory is approximately 2.6 GB, estimated (params x 0.5 bytes) from the cited 5.2 billion parameters.[9] Treat that calculation as a starting point for hardware budgeting, not a complete system requirement. Before choosing a local machine, check total memory consumption with your intended runtime and workload; the weight estimate alone does not establish a GPU or RAM requirement.

Consider this model for local audio experiments where the Apache-2.0 license meets your project’s requirements.[9] Evaluate it with representative audio and expected text outputs before committing to deployment. The practical caveat is “no public benchmark yet”: its ranking does not establish task accuracy. Use your own evaluation to decide whether its outputs justify the hardware allocation and integration work.

3. VibeVoice-ASR

VibeVoice-ASR by Microsoft ranks third under the ordering rule: newest release first, then downloads. Its Hugging Face publication date of March 2, 2026 follows the May and April releases from OpenMOSS-Team.[3][7][9] The ranking admits only audio text to text models with the Hugging Face pipeline tag audio-text-to-text. VibeVoice-ASR has no public benchmark yet, so its position does not establish a measured accuracy advantage.

Microsoft lists 8.3B parameters, a 128K-token context window, and an MIT license.[3] The BF16 weights occupy 16.7 GB.[3] Against the cited 8.3B parameter count, the 4-bit weight footprint is approximately 4.15 GB, estimated (params x 0.5 bytes).[3] Treat that calculation as a weight-storage estimate when planning local hardware. Allow additional memory for execution; neither the weight estimate nor the context window establishes a complete GPU VRAM or system RAM requirement.

For a local speech-transcription project, VibeVoice-ASR is a candidate to evaluate against your own recordings. Microsoft’s accompanying VIBEVOICE-ASR Technical Report is dated March 14, 2026.[4] The practical caveat is hardware sizing: a calculated quantized weight footprint does not demonstrate that a compatible quantized build exists or establish its runtime memory use. Check the intended runtime and measure memory consumption before committing to hardware.

4. ACE-Step Transcriber

ACE-Step Transcriber by ACE-Step ranks fourth under the ordering rule: newest release first, then downloads.[7][9][3][10][5][1] Its Hugging Face publication date is January 23, 2026.[10] The ranking admits only audio text to text models with the Hugging Face pipeline tag audio-text-to-text. ACE-Step Transcriber has no public benchmark yet, so its position reflects publication timing rather than demonstrated superiority in transcription quality.

The model has 10.7B parameters, with listed BF16 weights totaling 22.4 GB, and uses the MIT license.[10] Its estimated 4-bit weight memory is 5.35 GB, estimated (params x 0.5 bytes) from the cited 10.7B parameter count.[10] For local hardware planning, treat that estimate as a weight budget rather than a complete GPU or system RAM requirement. Select hardware with memory headroom, and verify the intended runtime and quantized checkpoint before committing to a machine.

Consider ACE-Step Transcriber for local transcription evaluations where MIT licensing suits your deployment requirements.[10] Evaluate representative recordings before adopting it: without a public benchmark, a claim that it is the right choice for your audio would be premature. The practical caveat is that the calculated memory budget does not establish a tested hardware configuration or guarantee that your intended setup will run successfully.

5. Music Flamingo

Music Flamingo by NVIDIA ranks fifth because its Hugging Face publication date falls between ACE-Step’s acestep-transcriber and NVIDIA’s Audio Flamingo 3: January 1, 2026, versus January 23, 2026, and October 24, 2025, respectively.[5][10][1] The ordering basis is newest release first, then downloads. The ranking admits only audio text to text models with the Hugging Face pipeline tag audio-text-to-text. Music Flamingo has no public benchmark yet in this September 25, 2026 comparison, so its position does not establish a performance advantage.[5]

Music Flamingo has 8.3 billion parameters, a context length of 1,200 tokens, and a listed BF16 weight size of 16.5 GB.[5] Its 4-bit weight memory is estimated (params x 0.5 bytes) at 4.15 GB, calculated from the cited 8.3 billion parameters.[5] Use that estimate as a starting point for local hardware planning, rather than a total RAM or GPU memory requirement. Leave room for runtime allocations before choosing hardware; the weight calculation alone cannot establish whether a particular device will run the model.

Music understanding is the intended application described by Music Flamingo: Scaling Music Understanding in Audio Language Models.[6] Consider it for that workload, while treating the 1,200-token context as a concrete caveat when planning prompts and responses.[5] Verify the license terms separately before deployment.

6. Audio Flamingo 3

Audio Flamingo 3 by NVIDIA ranks sixth under the ordering rule, newest release first, then downloads, with a Hugging Face publication date of October 24, 2025.[1] The ranking admits only audio text to text models carrying the Hugging Face pipeline tag audio-text-to-text. Its benchmark status is “no public benchmark yet,” so the position reflects release order rather than a demonstrated performance gap.

NVIDIA lists 8.3 billion parameters, a 32K-token context window and a BF16 weight download of 33.4 GB.[1] For hardware planning, quantized weight storage is approximately 4.15 GB at 4-bit, estimated (params x 0.5 bytes) from the cited 8.3 billion parameters.[1] Treat that calculation as a weight-storage estimate, not a complete GPU-memory or system-RAM requirement. Verify total memory consumption with your intended runtime before choosing hardware.

Consider Audio Flamingo 3 for local workflows that combine audio and text as input and need text as output.[1] The practical caveat is that the estimated quantized footprint does not establish whether a particular GPU can run your workload. Choose it when its input/output format and published context window match your task; the available benchmark status does not justify a claim of superior audio understanding.

How can you estimate memory requirements from parameter counts?

Estimate weight memory from the parameter count using estimated (params x 0.5 bytes) for a 4-bit representation; treat the result as a weight-storage estimate, not a complete runtime memory requirement.[7][9]

For OpenMOSS-Team’s MOSS-Transcribe-Diarize, the calculation is 909M parameters → 0.4545 GB, estimated (params x 0.5 bytes).[7] For OpenMOSS-Team’s MOSS-Audio-4B-Instruct, use 5.2B parameters → 2.6 GB, estimated (params x 0.5 bytes).[9] Use the published parameter count for the calculation rather than interpreting the model’s name as its parameter count.

Microsoft’s VibeVoice-ASR-HF gives another example: 8.3B parameters → 4.15 GB, estimated (params x 0.5 bytes).[3] Keep that calculated value separate from the listed BF16 weight size of 16.7 GB.[3] The calculation does not establish that a compatible quantized download exists or that a particular runtime can load it.

When planning a local deployment, use the estimate as a starting point for checking the actual model files and runtime. Avoid turning the weight estimate directly into a GPU or system RAM recommendation. Validate memory use with your intended audio input and context settings before committing hardware. Report any measured runtime requirement separately, including the configuration used, so readers can distinguish a calculation from an observed result.

Which local audio language models have longer context windows?

Microsoft’s VibeVoice-ASR-HF and OpenMOSS-Team’s MOSS-Transcribe-Diarize offer longer context windows: both list 128K tokens, compared with 32K tokens for NVIDIA’s Audio Flamingo 3 (nvidia/audio-flamingo-3-hf).[3][7][1] For a local workflow where token capacity is a selection criterion, start by comparing those models.

NVIDIA’s Music Flamingo (nvidia/music-flamingo-2601-hf) lists a context window of 1,200 tokens.[5] Its listed capacity is below the windows for Audio Flamingo 3, VibeVoice-ASR-HF and MOSS-Transcribe-Diarize.[5][1][3][7] Treat that comparison as a context-capacity comparison, rather than an accuracy ranking.

The matching context windows come with different parameter counts. MOSS-Transcribe-Diarize has 909M parameters and an Apache-2.0 license.[7] VibeVoice-ASR-HF has 8.3B parameters and an MIT license.[3] Audio Flamingo 3 and Music Flamingo each have 8.3B parameters despite their different listed context limits.[1][5]

For deployment planning, check the intended runtime’s supported context configuration and audio-input limits separately. Avoid converting a token limit directly into minutes of recording or treating it as a guarantee of transcription quality. Choose a candidate using the listed context capacity, then validate the intended recording length and workload on your local setup.

Which license terms should you check before choosing a local audio language model?

Check the license attached to the exact model repository, including permission for commercial use, modification and redistribution, plus any notice requirements, before choosing a local audio language model.

Microsoft’s VibeVoice-ASR-HF uses the MIT license.[3] ACE-Step’s acestep-transcriber also uses MIT.[10] Read the license text before distributing weights or bundling a model with an application. Check which copyright and permission notices you must preserve, and distinguish internal deployment from redistribution when reviewing your intended use.

OpenMOSS-Team’s MOSS-Transcribe-Diarize uses Apache-2.0,[7] as does OpenMOSS-Team’s MOSS-Audio-4B-Instruct.[9] Review the applicable notice, attribution, modification and patent provisions. Include any planned fine-tuning or distribution of modified weights in that review, rather than checking only whether you can download and run the original model.

For NVIDIA’s music-flamingo-2601-hf and audio-flamingo-3-hf, verify the license terms separately before adoption. Check the exact repository and revision you intend to deploy; do not infer permission from the availability of downloadable weights.

Keep a copy of the applicable license with your deployment records. Review accompanying code, dependencies and other bundled assets separately, and confirm that your intended handling of audio recordings is permitted. Treat the model license as one part of the deployment review, alongside permissions for the recordings you process.

Frequently Asked Questions

Which local audio language model ranks first, and why?

OpenMOSS-Team’s MOSS-Transcribe-Diarize (OpenMOSS-Team) ranks first because its Hugging Face publication date is the newest among the candidates. [7][9][3][10][5][1] The ranking admits only audio text to text models with the Hugging Face pipeline tag audio-text-to-text. The ordering basis is newest release first, then downloads. Downloads serve only as a tiebreak; placement does not establish a benchmark performance advantage.

What is the complete ranking order?

The order is MOSS-Transcribe-Diarize (OpenMOSS-Team) [7], OpenMOSS-Team’s MOSS-Audio-4B-Instruct (OpenMOSS-Team) [9], Microsoft’s VibeVoice-ASR (Microsoft)-HF [3], ACE-Step’s ACE-Step/acestep-transcriber [10], NVIDIA’s nvidia/music-flamingo-2601-hf [5], and NVIDIA’s nvidia/audio-flamingo-3-hf [1]. Read the sequence as a publication-recency ranking. Parameter count does not determine placement, and download popularity does not establish audio quality. Choose among the candidates using the context and license requirements of your application.

How much memory should I budget for quantized weights?

MOSS-Transcribe-Diarize has 909M parameters: 4-bit weight memory is 0.4545 GB, estimated (params x 0.5 bytes). [7] MOSS-Audio-4B-Instruct has 5.2B parameters: 4-bit weight memory is 2.6 GB, estimated (params x 0.5 bytes). [9] VibeVoice-ASR-HF has 8.3B parameters: 4-bit weight memory is 4.15 GB, estimated (params x 0.5 bytes). [3] Treat those calculations as weight budgets, not total-memory requirements or guarantees that a particular GPU can run the model.

Which models offer a long context window?

MOSS-Transcribe-Diarize and VibeVoice-ASR-HF each list a context length of 128K tokens. [7][3] Audio Flamingo 3 lists 32K tokens, while Music Flamingo lists 1,200 tokens. [1][5] Use those token limits when comparing context requirements. Avoid translating them directly into minutes of audio or assuming they establish transcription accuracy; neither conclusion follows from a token count alone.

What licenses do these models use?

MOSS-Transcribe-Diarize and MOSS-Audio-4B-Instruct use Apache-2.0. [7][9] VibeVoice-ASR-HF and ACE-Step/acestep-transcriber use MIT. [3][10] For NVIDIA’s Music Flamingo and Audio Flamingo 3 [5][1], verify the license terms separately before choosing a deployment. Review the applicable terms for your intended use rather than treating inclusion in a local-model ranking as permission to redistribute weights or incorporate them into a product.

Are there dated benchmark results that justify the ranking?

The benchmark status for every ranked candidate is “no public benchmark yet” at the September 25, 2026 cutoff. [7][9][3][10][5][1] No public leaderboard scores these candidates against one another, so the order does not express measured performance superiority. Any model-card benchmark must be labeled “self-reported.” A paper’s publication date alone cannot establish a dated benchmark result.

Sources

1. nvidia/audio-flamingo-3-hf model card (Hugging Face) — 2026-09-25
2. Audio Flamingo 3: Advancing Audio Intelligence with Fully Open Large Audio Language Models — 2025-07-10
3. microsoft/VibeVoice-ASR-HF model card (Hugging Face) — 2026-09-25
4. VIBEVOICE-ASR Technical Report — 2026-03-14
5. nvidia/music-flamingo-2601-hf model card (Hugging Face) — 2026-09-25
6. Music Flamingo: Scaling Music Understanding in Audio Language Models — 2025-11-13
7. OpenMOSS-Team/MOSS-Transcribe-Diarize model card (Hugging Face) — 2026-09-25
8. MOSS Transcribe Diarize: Accurate Transcription with Speaker Diarization — 2026-01-04
9. OpenMOSS-Team/MOSS-Audio-4B-Instruct model card (Hugging Face) — 2026-09-25
10. ACE-Step/acestep-transcriber model card (Hugging Face) — 2026-09-25
11. ACE-Step 1.5: Pushing the Boundaries of Open-Source Music Generation — 2026-01-31