mirror of
https://github.com/Nighthawk42/Qwen3-TTS-streaming.git
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Kedara Studios
f83f18439d
Streaming inference (voice clone) - repetition penalty: Without repetition penalty, the model can fall into a degenerate state where it keeps sampling the same codec tokens over and over. This manifests as: - Looping audio: the same syllable or sound fragment repeats endlessly - Extremely long generation: instead of reaching EOS in ~200-500 frames, it runs for thousands of frames (up to max_frames=10000) - Apparent "slowness": a response that should take ~1s of audio takes 10-30s to generate The fix works by tracking previously generated first-codebook token IDs and penalizing them before sampling: - Tokens with positive logits get divided by repetition_penalty (lowering their probability) - Tokens with negative logits get multiplied by it (pushing them further down) This nudges the model away from re-selecting the same tokens, so it progresses through the text naturally and reaches EOS in a reasonable number of steps rather than looping. Default is 1.0 (disabled) and is exposed through the supported_params whitelist in stream_generate_voice_clone() so it can be set via generate_config or user kwargs. Upstream sync (QwenLM/Qwen3-TTS): - Bump version 0.0.4 -> 0.1.1 to match upstream release. - finetuning/sft_12hz.py: weight sub-talker loss by 0.3 factor to prevent the code predictor gradient from dominating the main talker loss during SFT. - finetuning/sft_12hz.py: remove sub-codebook embedding accumulation loop (codec groups 1-15) from input embeddings, unnecessary and harmful for finetuning convergence (upstream PR #178). - finetuning/README.md: update recommended hyperparameters to batch_size=32, lr=2e-6, num_epochs=10 for more stable training.
121 lines
3.1 KiB
Markdown
121 lines
3.1 KiB
Markdown
## Fine Tuning Qwen3-TTS-12Hz-1.7B/0.6B-Base
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The Qwen3-TTS-12Hz-1.7B/0.6B-Base model series currently supports single-speaker fine-tuning. Please run `pip install qwen-tts` first, then run the command below:
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```
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git clone https://github.com/QwenLM/Qwen3-TTS.git
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cd Qwen3-TTS/finetuning
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```
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Then follow the steps below to complete the entire fine-tuning workflow. Multi-speaker fine-tuning and other advanced fine-tuning features will be supported in future releases.
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### 1) Input JSONL format
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Prepare your training file as a JSONL (one JSON object per line). Each line must contain:
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- `audio`: path to the target training audio (wav)
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- `text`: transcript corresponding to `audio`
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- `ref_audio`: path to the reference speaker audio (wav)
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Example:
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```jsonl
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{"audio":"./data/utt0001.wav","text":"其实我真的有发现，我是一个特别善于观察别人情绪的人。","ref_audio":"./data/ref.wav"}
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{"audio":"./data/utt0002.wav","text":"She said she would be here by noon.","ref_audio":"./data/ref.wav"}
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```
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`ref_audio` recommendation:
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- Strongly recommended: use the same `ref_audio` for all samples.
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- Keeping `ref_audio` identical across the dataset usually improves speaker consistency and stability during generation.
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### 2) Prepare data (extract `audio_codes`)
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Convert `train_raw.jsonl` into a training JSONL that includes `audio_codes`:
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```bash
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python prepare_data.py \
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--device cuda:0 \
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--tokenizer_model_path Qwen/Qwen3-TTS-Tokenizer-12Hz \
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--input_jsonl train_raw.jsonl \
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--output_jsonl train_with_codes.jsonl
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```
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### 3) Fine-tune
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Run SFT using the prepared JSONL:
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```bash
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python sft_12hz.py \
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--init_model_path Qwen/Qwen3-TTS-12Hz-1.7B-Base \
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--output_model_path output \
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--train_jsonl train_with_codes.jsonl \
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--batch_size 32 \
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--lr 2e-6 \
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--num_epochs 10 \
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--speaker_name speaker_test
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```
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Checkpoints will be written to:
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- `output/checkpoint-epoch-0`
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- `output/checkpoint-epoch-1`
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- `output/checkpoint-epoch-2`
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- ...
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### 4) Quick inference test
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```python
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import torch
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import soundfile as sf
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from qwen_tts import Qwen3TTSModel
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device = "cuda:0"
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tts = Qwen3TTSModel.from_pretrained(
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"output/checkpoint-epoch-2",
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device_map=device,
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dtype=torch.bfloat16,
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attn_implementation="flash_attention_2",
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)
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wavs, sr = tts.generate_custom_voice(
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text="She said she would be here by noon.",
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speaker="speaker_test",
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)
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sf.write("output.wav", wavs[0], sr)
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```
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### One-click shell script example
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```bash
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#!/usr/bin/env bash
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set -e
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DEVICE="cuda:0"
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TOKENIZER_MODEL_PATH="Qwen/Qwen3-TTS-Tokenizer-12Hz"
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INIT_MODEL_PATH="Qwen/Qwen3-TTS-12Hz-1.7B-Base"
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RAW_JSONL="train_raw.jsonl"
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TRAIN_JSONL="train_with_codes.jsonl"
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OUTPUT_DIR="output"
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BATCH_SIZE=2
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LR=2e-5
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EPOCHS=3
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SPEAKER_NAME="speaker_1"
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python prepare_data.py \
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--device ${DEVICE} \
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--tokenizer_model_path ${TOKENIZER_MODEL_PATH} \
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--input_jsonl ${RAW_JSONL} \
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--output_jsonl ${TRAIN_JSONL}
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python sft_12hz.py \
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--init_model_path ${INIT_MODEL_PATH} \
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--output_model_path ${OUTPUT_DIR} \
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--train_jsonl ${TRAIN_JSONL} \
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--batch_size ${BATCH_SIZE} \
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--lr ${LR} \
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--num_epochs ${EPOCHS} \
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--speaker_name ${SPEAKER_NAME}
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``` |