diodiogod-TTS-Audio-Suite/docs/MOSS_TRAINING_GUIDE.md

MOSS Training Guide

This guide explains the practical dataset meaning of the current MOSS training nodes in TTS Audio Suite.

Use this if 🧾 MOSS Dataset Rows feels unclear.

Current Training Scope

Current first training slice supports:

• MOSS-TTS 8B v1.0 and v1.5 (Delay)
• LoRA adapter training

The model selected on the connected MOSS engine is used for dataset preparation and training. Prepare the dataset again after switching between v1.0 and v1.5.

It does not currently support:

• Local 1.7B training
• TTSD training
• full-parameter training from the node path

Normal Training Flow

Current ComfyUI flow:

1. 🎞️ MOSS Clip Staging
2. 🧾 MOSS Dataset Rows
3. 📦 MOSS Dataset Prep
4. 🎛️ MOSS Training Config
5. 🎓 Model Training

The Important Fields

text_lines

This is the easiest one:

• one line per staged clip
• each line is the transcript/text of that clip

Example:

• clip001 audio says: Hello there.
• clip002 audio says: How are you?

Then:

Hello there.
How are you?

That is the normal default dataset shape:

• audio + transcript

For many voice LoRAs, this is enough.

reference_clip_lines

This is advanced. Most users should leave it blank at first.

It means:

• which other staged clip should be used as the reference audio for that row

That teaches the model a different pattern:

• text says what to say
• reference audio says whose voice/style to follow
• target audio is the correct output

Example:

• clip001 = Bob saying: My name is Bob.
• clip002 = Bob saying: Welcome to the show.

Row 2 can be:

• target audio = clip002
• text = Welcome to the show.
• reference = clip001

That teaches:

• “use clip001 as the reference voice, then produce the target line from clip002”

When To Ignore reference_clip_lines

Ignore it for:

• first MOSS LoRA tests
• single-speaker voice LoRAs
• fixed-voice adaptation
• plain language/domain adaptation

That means:

• just use audio + transcript
• maybe also use instruction

This is the best default.

When To Use reference_clip_lines

Use it only if you deliberately want reference-conditioned training.

That means teaching the model:

• how to use a separate reference clip for voice cloning
• how to follow reference style/prosody
• how to pick speaker identity from reference audio

This makes more sense for:

• reference-conditioned voice cloning behavior
• multi-speaker conditioning datasets
• style-following datasets

How A Model Learns Voice Cloning

Think of it this way:

• if a TTS model only sees audio + transcript, it learns how to do TTS for the voices/styles present in the data
• if a TTS model also sees ref_audio, it can learn a different pattern:
  • text says what to say
  • reference audio says whose voice/style to imitate
  • target audio is the correct output

That is how a model can learn reference-conditioned voice cloning, not just plain TTS.

So if your goal is to fine-tune that specific ability:

• “give the model a reference clip and make it speak in that voice/style”

then ref_audio is when this field makes sense.

If your goal is only:

• “make the model speak in this one fixed voice better”

then ref_audio usually does not matter much, and plain audio + transcript is the better starting point.

instruction vs ref_audio

These are different tools.

Use instruction for explicit text labels

Examples:

• speaking angrily
• whispering
• calm narration

This is usually clearer when you want to label the desired behavior directly.

Use quality for recording/presentation quality

Examples:

• telephone call quality
• studio recording
• noisy room recording

This is better when the label is about how the audio sounds as a recording or presentation format, not about acting/performance.

Use ref_audio for example audio conditioning

This is for:

• “sound like this other clip”
• “follow this speaker/style from audio”

So for many datasets:

• instruction is the simpler control
• ref_audio is the more advanced control

Practical Recommendations

Best first test

Use:

• staged clips
• correct transcripts
• no reference clips
• optional instruction only if clearly useful

In other words:

• text_lines: yes
• reference_clip_lines: leave blank

If training a single person's voice

Start with:

• audio + transcript

Do not add ref_audio unless you specifically want to teach reference-conditioned cloning behavior.

If training language adaptation like the Norwegian LoRA

Start with:

• audio + transcript

That is the normal base case.

You can still use reference audio later at inference time, because MOSS supports optional reference-based cloning, but the training dataset does not need to be built around ref_audio.

Validation In Plain Terms

📦 MOSS Dataset Prep also mentions validation.

That means:

• a small holdout part of the dataset used only to check training progress
• not used to update weights

If you do not have a separate validation manifest:

• leave validation_source blank
• let the node auto-split the main dataset

Short Version

If you want the least confusing starting point:

• use 🎞️ MOSS Clip Staging
• use 🧾 MOSS Dataset Rows
• fill only text_lines
• leave reference_clip_lines blank
• train the LoRA

That is the correct first path for most users.