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MiniMax H3: extra/repeated Mandarin dialogue and clipped speech onset when using standalone reference audio

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Evaluación

Dificultad
4/5
Tiempo estimado
3-5 días
Aptitud para principiantes
35/100
Tipo de issue
Error
Claridad
Bastante claro
Estado de actividad
Activo
Stack tecnológico
python
Área
ai

Línea de trabajo

Start with the attached native A/B/C workflows and validation.json; GPU execution has not been performed, so first run the documented workflows and retain the MP4s and console logs. Compare A with B and C while keeping the listed settings fixed, then check whether the reported extra dialogue, repetition, or clipped onset can be reproduced. Done means a reproducible result or clear guidance on supported reference audio and prompting; the issue does not identify a repository file or test to change.

Escrito por el modelo de indexación a partir del texto del issue.

Descripción

MiniMax_H3_reference_audio_repro.zip

MiniMax_H3_reference_audio_repro.zip

Summary

When using standalone reference audio for a character's voice, I have encountered extra opening dialogue, repeated words/sentences, and clipped or missing initial syllables. Disabling reference audio has been the practical workaround in my project, although this makes voice consistency harder.

I initially encountered this in a third-party Director workflow. I also tried the native official reference-to-video workflow and reported similar unwanted speech. I am not yet certain whether the cause is model behavior, the reference clip, prompt formatting, quantization, or the integration.

Evidence limitation: I deleted the abnormal output videos. The attached bundle contains inputs and newly prepared A/B workflows, not a recorded successful reproduction. These exact packaged workflows have been checked structurally but have not yet been run on the GPU. My earlier native-workflow test is a historical observation, not a verified result for the attached native baseline.

Observed behavior in earlier tests

  • An opening phrase such as 听说这个电话亭 could be generated even though it was not the requested dialogue.
  • The start of the requested sentence could be clipped or lose initial syllables.
  • Other runs repeated parts of the requested dialogue multiple times.
  • Replacing the voice reference improved the clipped-onset problem in one run, but did not establish a stable fix; repetition occurred in later testing.
  • A one-second opening with environment sound only was requested. In an earlier generated clip, speech-like audio started around 0.4–0.5 seconds. That clip is no longer available, so this timing cannot be independently verified from this bundle.

These symptoms occurred across different runs; I am not claiming every run exhibits all symptoms or that timing drift alone proves an implementation bug. I do not have a measured failure rate.

Expected behavior

For a voice/timbre reference, the generated speech should follow the specified dialogue without introducing reference-derived or unsolicited words or repeating the sentence. If the model only supports a different use of audio references, please clarify the supported prompting pattern and limitations.

Prepared core-only reproduction

The native_*.json files use native ComfyUI nodes and do not require Director, KJNodes, Seed-VC, or a custom importer. They are adapted from:

https://github.com/Comfy-Org/workflow_templates/blob/main/templates/video_minimax_h3_r2v.json

  1. Copy the bundle's input/h3_audio_repro directory into ComfyUI/input/ without renaming its files.
  2. Install the model files named in the workflow loaders.
  3. Load native_A_with_audio.json. Confirm the reference audio connects to MiniMaxH3ReferenceToVideo.ref_audios.ref_audio_0. Run and retain the MP4 and console log.
  4. Run native_B_no_audio_same_prompt.json. It keeps the same prompt, model, seed, steps, images, frame length, and sampler settings, but disconnects the reference-audio input. The unused LoadAudio node remains in the graph. Output prefixes differ so results do not overwrite one another.
  5. Since B deliberately leaves an unbound <Audio 1> mention in the prompt, also run native_C_no_audio_clean_prompt.json. C removes the introductory reference-audio instructions and preserves the dialogue block and scene instructions.
  6. If necessary, repeat with voice_alternative.wav selected in A's LoadAudio node. Record each result separately; do not mix sample changes into the same A/B comparison.

The output MP4s are written under ComfyUI/output/video/ with H3_audio_repro_ prefixes. To check more seeds, change the seed identically across A/B/C and keep all other settings fixed.

Prepared native baseline settings
  • Diffusion model: minimax_h3_ref2va_pruned_int8_convrot.safetensors
  • Text encoder: qwen3vl_32b_minimax_h3_nvfp4_awq.safetensors, type minimax
  • Video VAE: minimax_h3_video_vae_int8_convrot.safetensors
  • Audio VAE: minimax_h3_audio_vae_fp32.safetensors
  • Seed: 666, fixed; sampler: res_multistep; scheduler: simple; steps: 20; denoise: 1
  • Turbo/LoRA: removed from this isolated baseline
  • Resolution: official template selector at 0.4 megapixels, 16:9; frame rate: 24
  • Requested scene duration: 13 seconds; valid H3 frame length: 328 frames (approximately 13.667 seconds)
  • Five image references, one optional standalone audio reference, no reference video or video soundtrack

These are the prepared baseline settings, not a claim that every earlier test used this configuration. The historical Director path used a hybrid model and FL2V Turbo LoRA. Its separate director_A_with_audio.json and director_B_no_audio.json are included only as supplementary cases and require the current Director plugin.

Prompt and reference audio

The full original Mandarin prompt is in prompt_original.txt and embedded in the workflows. It uses <Audio 1>, (S3), and one <d>[Chinese] ... </d> dialogue block. It asks for the reference's voice/timbre rather than its words. There is only one dialogue block; the visual description contains semantic references to parts of the same sentence, which may be relevant to repetition.

Both reference clips are extracted from earlier generated video, rather than clean studio recordings. They may contain ambient sound:

  • voice_original.wav: 3.2 seconds, 32 kHz, mono, PCM 16-bit.
  • voice_alternative.wav: approximately 2.333 seconds, 32 kHz, stereo, PCM 16-bit.

The alternate clip was cut at a generated segment boundary; no speech transcription or source separation was performed. File hashes and formats are listed in assets_manifest.json. Please advise if reference length, background sound, or the nonconsecutive speaker ID (S3) is unsupported or requires different formatting.

Historical troubleshooting

  • Normalized Speaker ID notation and retained the character's (S3) identity.
  • Tried disabling acceleration and increasing sampling steps; the issue was not reliably eliminated.
  • Tried a pure Ref2VA model as well as the hybrid workflow.
  • Tried the native official workflow as well as the Director path.
  • Replaced the reference clip and adjusted opening-silence instructions.

I cannot provide controlled per-configuration result videos for these historical tests, because the outputs were deleted. I am not attributing the behavior to a specific change based only on these experiments.

Environment

Values recovered from an earlier ComfyUI startup log, not freshly collected from a new reproduction:

  • OS: Windows
  • ComfyUI: 0.41.3
  • Python: 3.13.12
  • PyTorch: 2.12.1+cu130
  • GPU: NVIDIA GeForce RTX 4090 D; reported VRAM: 23028 MB

Exact current ComfyUI commit, frontend version, driver version, command-line arguments, and a fresh startup log: not captured yet. Model weight hashes are also not captured. I will add current environment details and newly generated A/B output videos when available.

Questions

  1. Is standalone audio reference intended to preserve only voice identity while independently generating the supplied dialogue?
  2. Is there a recommended reference length, sample format, or prompt structure for Mandarin speech?
  3. Are extra/repeated dialogue or unstable speech onset known limitations with these weights or quantized encoders?
  4. Is there a recommended model/integration revision to test next?

The bundle's validation.json records static checks and explicitly marks GPU execution as not performed.

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