[Model] Fun cosy voice3-0.5-b-2512 by divyanshsinghvi · Pull Request #498 · vllm-project/vllm-omni

divyanshsinghvi · 2025-12-27T09:59:55Z

Purpose

Resolves #315

This PR integrates the CosyVoice3 text-to-speech model into vllm-omni, implementing both the "Talker" (LLM) and "Code2Wav" (Flow Matching + HiFiGAN) stages. It includes critical
architectural enhancements to ensure stability and correctness within the vLLM execution engine.

Model Implementation

Added CosyVoice3Model supporting multi-stage execution:
- Talker Stage: Generates speech tokens from text using a Qwen2 backbone.
- Code2Wav Stage: Converts speech tokens to waveforms using a DiT-based Flow Matching decoder and HiFiGAN vocoder.
Integrated CosyVoice3MultiModalProcessor for handling audio inputs and feature extraction.

Current Limitations:

Single Sample Support: The implementation currently supports a batch size of 1 only. Proper batching logic and verification can be handled in a separate, subsequent PR.
Scope: Limited to Zero-Shot mode; support for other CosyVoice variants is not yet implemented but would be a good first issue with minimal changes required.

Test Plan

python examples/offline_inference/text_to_speech/verify_e2e_cosyvoice.py --model pretrained_models/Fun-CosyVoice3-0.5B --tokenizer pretrained_models/Fun-CosyVoice3-0.5B/CosyVoice-BlankEN

Test Result

Input: prompt.wav
Output: output_0.wav

Performance Benchmarks:

Run: NVIDIA GeForce RTX 3070
CUDA Version : 13.0
Driver Version : 580.95.05

Stats [latest to earlier]:

After fixing code to allow enforce_eager=False

======================================================================
BENCHMARK RESULTS (10/10 successful runs)
======================================================================

END-TO-END METRICS:
----------------------------------------
  Time (ms):     mean=4648.44, std=152.61, min=4457.91, max=5031.88, median=4637.42
  Total tokens:  mean=171.0, std=0.0, min=171, max=171

STAGE 0 (LLM) METRICS:
----------------------------------------
  Time (ms):     mean=4005.45, std=140.03, min=3834.02, max=4360.00, median=3989.22
  Tokens out:    mean=88.0, std=0.0, min=88, max=88

STAGE 1 (AUDIO SYNTHESIS) METRICS:
----------------------------------------
  Time (ms):     mean=641.88, std=14.44, min=622.78, max=670.87, median=643.62

TRANSFER METRICS (Stage 0 -> Stage 1):
----------------------------------------
  Time (ms):     mean=2.301, std=0.084, min=2.166, max=2.438
  Throughput:    mean=2924.54, std=107.69, min=2756.57, max=3102.47 Mbps

TIME BREAKDOWN (average):
----------------------------------------
  Stage 0:       4005.45 ms (86.2%)
  Stage 1:       641.88 ms (13.8%)
  Transfer:      2.301 ms (0.05%)
  Total E2E:     4648.44 ms

======================================================================

Integration of vllm Qwen2Model for Stage 0 .

Stage 0: Memory Spiked integrating vllm implementation from vllm.model_executor.models.qwen2 import Qwen2Model compared to using from transformers import Qwen2ForCausalLM. Unsure Why?

No effect on runtime.

Before integration of vllm Qwen2Model

For E2E time metrics, memory profiling was off.

======================================================================
BENCHMARK RESULTS (10/10 successful runs)
======================================================================

END-TO-END METRICS:
----------------------------------------
  Time (ms):     mean=6297.54, std=163.46, min=6050.48, max=6581.21, median=6306.11
  Total tokens:  mean=171.0, std=0.0, min=171, max=171

STAGE 0 (LLM) METRICS:
----------------------------------------
  Time (ms):     mean=5442.82, std=153.97, min=5217.16, max=5722.37, median=5449.38
  Tokens out:    mean=88.0, std=0.0, min=88, max=88

STAGE 1 (AUDIO SYNTHESIS) METRICS:
----------------------------------------
  Time (ms):     mean=853.59, std=20.55, min=832.21, max=904.91, median=847.82

TRANSFER METRICS (Stage 0 -> Stage 1):
----------------------------------------
  Time (ms):     mean=2.356, std=0.158, min=2.176, max=2.749
  Throughput:    mean=2862.73, std=176.95, min=2444.57, max=3087.85 Mbps

TIME BREAKDOWN (average):
----------------------------------------
  Stage 0:       5442.82 ms (86.4%)
  Stage 1:       853.59 ms (13.6%)
  Transfer:      2.356 ms (0.04%)
  Total E2E:     6297.54 ms

======================================================================

Memory :

  Summary - Total VRAM Usage:                                                                                                                                                           
  ┌─────────────────────────┬────────────────────────────────────┐                                                                                                                      
  │          Stage          │            Peak Memory             │                                                                                                                      
  ├─────────────────────────┼────────────────────────────────────┤                                                                                                                      
  │ Stage-0 (LLM)           │ ~2.0 GB                            │                                                                                                                      
  ├─────────────────────────┼────────────────────────────────────┤                                                                                                                      
  │ Stage-1 (DiT + HiFiGAN) │ ~1.68 GB                           │                                                                                                                      
  ├─────────────────────────┼────────────────────────────────────┤                                                                                                                      
  │ Combined                │ ~3.7 GB (sequential, not additive) │                                                                                                                      
  └─────────────────────────┴────────────────────────────────────┘

Memory Profiling:

Stage 0 :

Stage 1:

Essential Elements of an Effective PR Description Checklist

The purpose of the PR, such as "Fix some issue (link existing issues this PR will resolve)".
The test plan, such as providing test command.
The test results, such as pasting the results comparison before and after, or e2e results
(Optional) The necessary documentation update, such as updating supported_models.md and examples for a new model.
(Optional) Release notes update. If your change is user facing, please update the release notes draft.

BEFORE SUBMITTING, PLEASE READ https://github.com/vllm-project/vllm-omni/blob/main/CONTRIBUTING.md (anything written below this line will be removed by GitHub Actions)

Signed-off-by: Divyansh Singhvi <divyanshsinghvi@gmail.com>

…ress Signed-off-by: Divyansh Singhvi <divyanshsinghvi@gmail.com>

Signed-off-by: Divyansh Singhvi <divyanshsinghvi@gmail.com>

…n also fixed Signed-off-by: Divyansh Singhvi <divyanshsinghvi@gmail.com>

Signed-off-by: Divyansh Singhvi <divyanshsinghvi@gmail.com>

…/vllm-omni into Fun-CosyVoice3-0.5B-2512

examples/offline_inference/text_to_speech/verify_e2e_cosyvoice.py

vllm_omni/model_executor/models/cosyvoice3/config.py

vllm_omni/model_executor/models/cosyvoice3/dit.py

vllm_omni/model_executor/models/cosyvoice3/cosyvoice3_talker.py

vllm_omni/model_executor/models/cosyvoice/llm.py

vllm_omni/model_executor/stage_configs/cosyvoice3.yaml

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vllm_omni/model_executor/models/cosyvoice3/cosyvoice3.py

Signed-off-by: dsinghvi <divyanshsinghvi@gmail.com>

Signed-off-by: Divyansh Singhvi <divyanshsinghvi@gmail.com>

Signed-off-by: dsinghvi <divyanshsinghvi@gmail.com>

divyanshsinghvi · 2026-02-24T15:05:50Z

@linyueqian All the comments are addressed.
Also made it compatible with the vllm 0.16.0 api changes.

cc: @hsliuustc0106

Signed-off-by: dsinghvi <divyanshsinghvi@gmail.com>

linyueqian

LGTM

linyueqian · 2026-02-25T05:12:29Z

@hsliuustc0106

divyanshsinghvi · 2026-03-01T22:06:17Z

@hsliuustc0106

linyueqian · 2026-03-01T22:25:05Z

Great work! A few things worth tracking as follow-ups:

Async chunk streaming - code2wav currently waits for all tokens; adding async_chunk + a frame-aligned stage processor (like Qwen3-TTS) would reduce TTFP significantly.
vLLM-native talker - swapping HF Qwen2ForCausalLM for vLLM's native model with stacked_params_mapping would unlock quantization and memory efficiency.
CUDA graph for code2wav - worth investigating if runtime_info access can be refactored to allow graph capture.

divyanshsinghvi · 2026-03-01T23:30:28Z

Great work! A few things worth tracking as follow-ups:

Async chunk streaming - code2wav currently waits for all tokens; adding async_chunk + a frame-aligned stage processor (like Qwen3-TTS) would reduce TTFP significantly.

vLLM-native talker - swapping HF Qwen2ForCausalLM for vLLM's native model with stacked_params_mapping would unlock quantization and memory efficiency.

CUDA graph for code2wav - worth investigating if runtime_info access can be refactored to allow graph capture.

Yes, I will work on it in a follow up PR after this is merged.
I updated to vLLM's native model here, but I will have to check for stacked_params_mapping.
CUDA Graph support is enabled, I added multiple fixes to support that a while ago. Was there some issue? I have tested with enforce_eager: False and it works and is significantly faster.

linyueqian · 2026-03-02T00:01:25Z

Could you share a quick benchmark with just these metrics?

TTFA (time to first audio)
RTF (real-time factor)

Please report for:

enforce_eager=True
enforce_eager=False

Same prompt/audio setup for both is enough.

divyanshsinghvi · 2026-03-02T00:07:11Z

Could you share a quick benchmark with just these metrics?

TTFA (time to first audio)

RTF (real-time factor)

Please report for:

enforce_eager=True

enforce_eager=False

Same prompt/audio setup for both is enough.

The ones in the description (#498 (comment)) don't suffice (Check the Performance Benchmarks: section)?
If not, I can do more clear Side by side comparison, but it shows clearly the jump for enforce_eager=False and previous setup with stage wise breakdown and transfer time

linyueqian · 2026-03-02T00:12:59Z

Thanks, I saw the E2E and stage-time benchmarks in the PR description.\nCould you also share TTFA and RTF (same setup) for completeness?

linyueqian · 2026-03-02T04:33:38Z

@hsliuustc0106

divyanshsinghvi · 2026-03-02T09:05:37Z

Thanks, I saw the E2E and stage-time benchmarks in the PR description.\nCould you also share TTFA and RTF (same setup) for completeness?

Will update.

divyanshsinghvi · 2026-03-02T12:23:30Z

Thanks, I saw the E2E and stage-time benchmarks in the PR description.\nCould you also share TTFA and RTF (same setup) for completeness?

I am just using the previous benchmark numbers to compute these results, Let me know if I misunderstood it:

enforce_eager = False

Audio duration = 88 / 25 = 3.52 seconds
TTFA = E2E time = 4648.44 ms (since non-streaming, first audio = complete audio)
RTF = generation_time / audio_duration = 4648.44 / 3520 = 1.32

enforce_eager = True

Audio duration = 88 / 25 = 3.52 seconds
TTFA = E2E time = 6297.54 ms (since non-streaming, first audio = complete audio)
RTF = generation_time / audio_duration = 6297.54 / 3520 = 1.79

@hsliuustc0106 @linyueqian

hsliuustc0106 · 2026-03-02T13:57:02Z

Thanks, I saw the E2E and stage-time benchmarks in the PR description.\nCould you also share TTFA and RTF (same setup) for completeness?

I am just using the previous benchmark numbers to compute these results, Let me know if I misunderstood it:

enforce_eager = False

Audio duration = 88 / 25 = 3.52 seconds

TTFA = E2E time = 4648.44 ms (since non-streaming, first audio = complete audio)

RTF = generation_time / audio_duration = 4648.44 / 3520 = 1.32

enforce_eager = True

Audio duration = 88 / 25 = 3.52 seconds

TTFA = E2E time = 6297.54 ms (since non-streaming, first audio = complete audio)

RTF = generation_time / audio_duration = 6297.54 / 3520 = 1.79

@hsliuustc0106 @linyueqian

we expect the RTF < 1.0 for a minimal requirement.

divyanshsinghvi · 2026-03-02T14:02:14Z

Thanks, I saw the E2E and stage-time benchmarks in the PR description.\nCould you also share TTFA and RTF (same setup) for completeness?

I am just using the previous benchmark numbers to compute these results, Let me know if I misunderstood it:
enforce_eager = False

Audio duration = 88 / 25 = 3.52 seconds

TTFA = E2E time = 4648.44 ms (since non-streaming, first audio = complete audio)

RTF = generation_time / audio_duration = 4648.44 / 3520 = 1.32

enforce_eager = True

Audio duration = 88 / 25 = 3.52 seconds

TTFA = E2E time = 6297.54 ms (since non-streaming, first audio = complete audio)

RTF = generation_time / audio_duration = 6297.54 / 3520 = 1.79

@hsliuustc0106 @linyueqian

we expect the RTF < 1.0 for a minimal requirement.

Wait, this will depend on which GPU you are using, if you run it on H200 you will have a better number there. Unsure how it can be standardised across GPU for RTF <1.0 requirement?

Also RTF makes more sense to be tracked in async TTS implementation for this model, which should be relatively faster as you will not have empty stages as right now.

Please note the above experiments are in 3070. I don't have capacity to rent out better hardware right now. But I feel the current requirement is slightly arbitrary.

What would you recommend @hsliuustc0106 @linyueqian , shall I close this PR given the current stats?

I don't see any direct improvements, I have done most of which I could see and is probably among few models in the vllm-omni which supports the CUDA graph optimization.

If you have any recommendations for performance optimization, I might try those.

hsliuustc0106 · 2026-03-02T14:16:47Z

Thanks, I saw the E2E and stage-time benchmarks in the PR description.\nCould you also share TTFA and RTF (same setup) for completeness?

I am just using the previous benchmark numbers to compute these results, Let me know if I misunderstood it:
enforce_eager = False

Audio duration = 88 / 25 = 3.52 seconds

TTFA = E2E time = 4648.44 ms (since non-streaming, first audio = complete audio)

RTF = generation_time / audio_duration = 4648.44 / 3520 = 1.32

enforce_eager = True

Audio duration = 88 / 25 = 3.52 seconds

TTFA = E2E time = 6297.54 ms (since non-streaming, first audio = complete audio)

RTF = generation_time / audio_duration = 6297.54 / 3520 = 1.79

@hsliuustc0106 @linyueqian

we expect the RTF < 1.0 for a minimal requirement.

Wait, this will depend on which GPU you are using, if you run it on H200 you will have a better number there. Unsure how it can be standardised across GPU for RTF <1.0 requirement?

Also RTF makes more sense to be tracked in async TTS implementation for this model, which should be relatively faster as you will not have empty stages as right now.

Please note the above experiments are in 3070. I don't have capacity to rent out better hardware right now. But I feel the current requirement is slightly arbitrary.

What would you recommend @hsliuustc0106 @linyueqian , shall I close this PR given the current stats?

I don't see any direct improvements, I have done most of which I could see and is probably among few models in the vllm-omni which supports the CUDA graph optimization.

If you have any recommendations for performance optimization, I might try those.

have you ever tried to adapt this model with async_chunk with streaming output?

divyanshsinghvi · 2026-03-02T14:18:56Z

Thanks, I saw the E2E and stage-time benchmarks in the PR description.\nCould you also share TTFA and RTF (same setup) for completeness?

I am just using the previous benchmark numbers to compute these results, Let me know if I misunderstood it:
enforce_eager = False

Audio duration = 88 / 25 = 3.52 seconds

TTFA = E2E time = 4648.44 ms (since non-streaming, first audio = complete audio)

RTF = generation_time / audio_duration = 4648.44 / 3520 = 1.32

enforce_eager = True

Audio duration = 88 / 25 = 3.52 seconds

TTFA = E2E time = 6297.54 ms (since non-streaming, first audio = complete audio)

RTF = generation_time / audio_duration = 6297.54 / 3520 = 1.79

@hsliuustc0106 @linyueqian

we expect the RTF < 1.0 for a minimal requirement.

Wait, this will depend on which GPU you are using, if you run it on H200 you will have a better number there. Unsure how it can be standardised across GPU for RTF <1.0 requirement?
Also RTF makes more sense to be tracked in async TTS implementation for this model, which should be relatively faster as you will not have empty stages as right now.
Please note the above experiments are in 3070. I don't have capacity to rent out better hardware right now. But I feel the current requirement is slightly arbitrary.
What would you recommend @hsliuustc0106 @linyueqian , shall I close this PR given the current stats?
I don't see any direct improvements, I have done most of which I could see and is probably among few models in the vllm-omni which supports the CUDA graph optimization.
If you have any recommendations for performance optimization, I might try those.

have you ever tried to adapt this model with async_chunk with streaming output?

That's already discussed that it will be a followup PR which should convert the sync to async. Any other suggestions regarding speed up.

divyanshsinghvi added 13 commits December 20, 2025 18:06

cosyvoice 3 initiated

00cf5fe

Signed-off-by: Divyansh Singhvi <divyanshsinghvi@gmail.com>

cosyvoice base file started

0ddcbbf

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Initialized model paths

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Initialized model paths

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renaming

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Merge branch 'main' into Fun-CosyVoice3-0.5B-2512

34bea22

Merge branch 'main' into Fun-CosyVoice3-0.5B-2512

769aaeb

stages are ready;

c1130e6

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Major fixes to stage e2e working; not correct audio yet but good prog…

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cosyvoice.yaml stage_configs

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stage input processor link function

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yaml updates and input handling corrected

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fixes to inputs prompt token embed and token embed fixed; speech toke…

ca2ee7f

…n also fixed Signed-off-by: Divyansh Singhvi <divyanshsinghvi@gmail.com>

david6666666 mentioned this pull request Dec 29, 2025

[RFC]: DiT model and feature support enhancement #85

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divyanshsinghvi added 7 commits December 29, 2025 21:23

Getting finally multimodal embeds correct

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llm part fixed now

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almost stage 1 half completed

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e2e working

46429c0

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e2e cosyvoice script some weird noise at end rest seems good

f481b38

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cosyvoice llm done

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cosyvoice config refactored

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hsliuustc0106 mentioned this pull request Dec 30, 2025

[RFC]: Does the official team of vllm-omni plan to support the integration of the index-tts model? #545

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divyanshsinghvi changed the title ~~[Draft] [Model] Fun cosy voice3-0.5-b-2512~~ [WIP] [Model] Fun cosy voice3-0.5-b-2512 Dec 30, 2025

divyanshsinghvi added 3 commits December 30, 2025 18:36

Merge branch 'main' into Fun-CosyVoice3-0.5B-2512

c74249f

registry cosyvoice everything upgraded

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Signed-off-by: Divyansh Singhvi <divyanshsinghvi@gmail.com>

Merge branch 'Fun-CosyVoice3-0.5B-2512' of github.com:divyanshsinghvi…

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…/vllm-omni into Fun-CosyVoice3-0.5B-2512

divyanshsinghvi commented Dec 30, 2025

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divyanshsinghvi marked this pull request as ready for review December 30, 2025 14:16

divyanshsinghvi requested a review from hsliuustc0106 as a code owner December 30, 2025 14:16

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divyanshsinghvi added 6 commits February 24, 2026 14:23

api changes

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get data parser changed

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ok

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remove precheck fil

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utils

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cached runtime components

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divyanshsinghvi requested a review from linyueqian February 24, 2026 15:05

divyanshsinghvi added 2 commits February 24, 2026 15:10

multimodal output not present?

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cosyvoice3.py

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linyueqian approved these changes Feb 25, 2026

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Merge branch 'main' into Fun-CosyVoice3-0.5B-2512

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Merge branch 'main' into Fun-CosyVoice3-0.5B-2512

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linyueqian mentioned this pull request Mar 2, 2026

Congratulations on the Fun-CosyVoice 3.5 Release! FunAudioLLM/CosyVoice#1840

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Merge branch 'main' into Fun-CosyVoice3-0.5B-2512

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Purpose

Model Implementation

Current Limitations:

Test Plan

Test Result

Performance Benchmarks:

Stats [latest to earlier]:

After fixing code to allow enforce_eager=False

Integration of vllm Qwen2Model for Stage 0 .

Before integration of vllm Qwen2Model

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