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Model: mesolitica/Malaysian-TTS-1.7B-v0.1 Source: Original Platform
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README.md
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---
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library_name: transformers
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new_version: mesolitica/Malaysian-TTS-1.7B-v1
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---
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# Malaysian-TTS-1.7B-v0.1
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Continue pretraining [Qwen/Qwen3-1.7B-Base](https://huggingface.co/Qwen/Qwen3-1.7B-Base) on [mesolitica/Malaysian-TTS-v2](https://huggingface.co/datasets/mesolitica/Malaysian-TTS-v2),
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1. Use [DistilCodec](https://github.com/IDEA-Emdoor-Lab/DistilCodec) as speech detokenizer, output in 24k sample rate.
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2. Optional controllable pitch and speed for each words.
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3. Support context switching between Malay and English.
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4. Support streamable text segment.
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5. Support `husein` and `idayu` speakers only.
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## How do we train
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1. Dataset purely synthetic generated using [mesolitica/Malaysian-Podcast-Dia-1.6B](https://huggingface.co/mesolitica/Malaysian-Podcast-Dia-1.6B).
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2. Multipacking with proper document masking on 4096 context length.
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3. FP32-BF16 mixed precision training.
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4. Full parameter finetuning.
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5. WanDB at https://wandb.ai/huseinzol05/Qwen-Qwen3-1.7B-Base-4k-TTS-distilcodec
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## How to use
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1. First install DistilCodec,
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```bash
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pip3 install git+https://github.com/mesolitica/DistilCodec
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```
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2. Load the models,
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```python
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# wget https://huggingface.co/IDEA-Emdoor/DistilCodec-v1.0/resolve/main/model_config.json
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# wget https://huggingface.co/IDEA-Emdoor/DistilCodec-v1.0/resolve/main/g_00204000
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from distilcodec import DistilCodec, demo_for_generate_audio_codes
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from transformers import AutoTokenizer, AutoModelForCausalLM
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codec_model_config_path='model_config.json'
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codec_ckpt_path = 'g_00204000'
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codec = DistilCodec.from_pretrained(
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config_path=codec_model_config_path,
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model_path=codec_ckpt_path,
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use_generator=True,
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is_debug=False).eval()
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tokenizer = AutoTokenizer.from_pretrained('mesolitica/Malaysian-TTS-1.7B-v0.1')
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model = AutoModelForCausalLM.from_pretrained('mesolitica/Malaysian-TTS-1.7B-v0.1', torch_dtype = 'auto').cuda()
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```
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### Non-streaming
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```bash
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import soundfile as sf
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string = 'The first anti-hoax legislation in the world, Akta Anti Berita Tidak Benar two thousand and eighteen. Saya nak makan nasi ayam.'
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left = 'idayu' +': ' + string
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prompt = f'<|im_start|>{left}<|speech_start|>'
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generate_kwargs = dict(
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**tokenizer(prompt, return_tensors = 'pt', add_special_tokens = False).to('cuda'),
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max_new_tokens=1024,
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temperature=0.5,
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do_sample=True,
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repetition_penalty=1.0,
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)
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generation_output = model.generate(**generate_kwargs)
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speech_token = tokenizer.decode(generation_output[0]).split('<|speech_start|>')[1].replace('<|endoftext|>', '')
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numbers = re.findall(r'speech_(\d+)', speech_token)
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d = list(map(int, numbers))
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y_gen = codec.decode_from_codes(d, minus_token_offset=False)
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sf.write('output.mp3', y_gen[0, 0].cpu().numpy(), 24000)
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```
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Output,
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1. [output-idayu.mp3](output-idayu.mp3)
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2. [output-husein.mp3](output-husein.mp3)
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### Streaming text context
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```python
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from tqdm import tqdm
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import numpy as np
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strings = [
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'The first anti-hoax legislation in the world,',
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'Akta Anti Berita Tidak Benar two thousand and eighteen.',
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'Saya nak makan nasi ayam,',
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'dan saya tak suka mandi.'
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]
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ys = []
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generation_output = None
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for no, string in tqdm(enumerate(strings)):
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if generation_output is None:
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left = 'streaming,idayu' +': ' + string
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prompt = f'<|im_start|>{left}<|speech_start|>'
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else:
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left = string
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prompt = f'{tokenizer.decode(generation_output[0])}{left}<|speech_start|>'
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generate_kwargs = dict(
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**tokenizer(prompt, return_tensors = 'pt', add_special_tokens = False).to('cuda'),
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max_new_tokens=1024,
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temperature=0.6,
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do_sample=True,
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repetition_penalty=1.,
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)
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generation_output = model.generate(**generate_kwargs)
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speech_token = tokenizer.decode(generation_output[0]).split('<|speech_start|>')[-1].replace('<|endoftext|>', '')
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numbers = re.findall(r'speech_(\d+)', speech_token)
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d = list(map(int, numbers))
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y_gen = codec.decode_from_codes(
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d,
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minus_token_offset=False
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)
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ys.append(y_gen[0, 0].cpu().numpy())
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sf.write('output.mp3', np.concatenate(ys), 24000)
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```
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Output,
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1. [output-idayu-chunk.mp3](output-idayu-chunk.mp3)
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2. [output-husein-chunk.mp3](output-husein-chunk.mp3)
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## Source code
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Source code at https://github.com/mesolitica/malaya-speech/tree/master/session/qwen-tts
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## Acknowledgement
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Special thanks to https://www.sns.com.my and Nvidia for 1x H100!
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config.json
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"architectures": [
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"Qwen3ForCausalLM"
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"attention_dropout": 0.0,
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"bos_token_id": 151643,
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"eos_token_id": 151643,
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 2048,
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"initializer_range": 0.02,
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"intermediate_size": 6144,
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"max_position_embeddings": 32768,
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"max_window_layers": 28,
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"model_type": "qwen3",
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"num_hidden_layers": 28,
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"rope_theta": 1000000,
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"tie_word_embeddings": true,
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"transformers_version": "4.51.3",
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"use_cache": true,
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"vocab_size": 184448
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}
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|
||||
}
|
||||
}
|
||||
BIN
output-husein.mp3
Normal file
BIN
output-husein.mp3
Normal file
Binary file not shown.
BIN
output-idayu.mp3
Normal file
BIN
output-idayu.mp3
Normal file
Binary file not shown.
31
special_tokens_map.json
Normal file
31
special_tokens_map.json
Normal file
@@ -0,0 +1,31 @@
|
||||
{
|
||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>",
|
||||
"<|object_ref_start|>",
|
||||
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|
||||
"<|box_start|>",
|
||||
"<|box_end|>",
|
||||
"<|quad_start|>",
|
||||
"<|quad_end|>",
|
||||
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|
||||
"<|vision_end|>",
|
||||
"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>"
|
||||
],
|
||||
"eos_token": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
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|
||||
},
|
||||
"pad_token": {
|
||||
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|
||||
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|
||||
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|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:80371fa6d38a5e80eb677e1158befbb3cca6b4ca200ef6b3447c7971725ba37a
|
||||
size 17737836
|
||||
3
tokenizer_config.json
Normal file
3
tokenizer_config.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:1051c39bb83fc281269157e62a8c72e8c9e1cf1435db2a4035936fdeb2edcdc6
|
||||
size 5997101
|
||||
BIN
vocab.json
(Stored with Git LFS)
Normal file
BIN
vocab.json
(Stored with Git LFS)
Normal file
Binary file not shown.
Reference in New Issue
Block a user