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Model: mesolitica/Malaysian-TTS-1.7B-v1 Source: Original Platform
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DisfluencySpeech-v1.mp3
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DisfluencySpeech-v1.mp3
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---
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library_name: transformers
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tags: []
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---
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# Malaysian-TTS-1.7B-v1
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Continue pretraining [mesolitica/Malaysian-TTS-1.7B-v0.1](https://huggingface.co/mesolitica/Malaysian-TTS-1.7B-v0.1) on much consistent dataset,
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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. Support context switching between Malay and English.
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3. Better pronunciation for letters.
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4. Better repetitive tolerance.
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## Speakers
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1. [husein](https://huggingface.co/datasets/mesolitica/Malaysian-TTS-v2)
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2. [idayu](https://huggingface.co/datasets/mesolitica/Malaysian-TTS-v2)
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3. [singaporean](https://huggingface.co/datasets/mesolitica/IMDA-TTS)
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4. [DisfluencySpeech](https://huggingface.co/datasets/amaai-lab/DisfluencySpeech)
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5. [singlish-speaker2050](https://huggingface.co/datasets/thucdangvan020999/singlish-speaker2050)
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6. [singlish-speaker2202](https://huggingface.co/datasets/thucdangvan020999/singlish-speaker2202)
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7. [haqkiem](https://www.linkedin.com/in/haqkiem-daim/), private dataset.
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## How do we train
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1. Multipacking with proper document masking on 4096 context length.
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2. FP32-BF16 mixed precision training.
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3. Full parameter finetuning.
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4. WanDB at https://wandb.ai/huseinzol05/Malaysian-TTS-1.7B-v1
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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-v1')
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model = AutoModelForCausalLM.from_pretrained('mesolitica/Malaysian-TTS-1.7B-v1', torch_dtype = 'auto').cuda()
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```
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3. Generate,
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```bash
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import soundfile as sf
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import re
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from tqdm import tqdm
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speakers = [
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'husein',
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'idayu',
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'singaporean',
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'DisfluencySpeech',
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'singlish-speaker2050',
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'singlish-speaker2202',
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'haqkiem',
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]
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string = 'IC saya adalah, sembilan enam, kosong tiga, satu empat, one, one, one, one, A, B, C, D, D, yes, Husein is very cute, cute, cute.'
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for s in tqdm(speakers):
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left = s +': ' + 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=0.7,
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do_sample=True,
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repetition_penalty=1.1,
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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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sf.write(f'{s}.mp3', y_gen[0, 0].cpu().numpy(), 24000)
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```
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Output,
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1. [husein-v1.mp3](husein-v1.mp3)
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2. [idayu-v1.mp3](idayu-v1.mp3)
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3. [singaporean-v1.mp3](singaporean-v1.mp3)
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4. [DisfluencySpeech-v1.mp3](DisfluencySpeech-v1.mp3)
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5. [singlish-speaker2050-v1.mp3](singlish-speaker2050-v1.mp3)
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6. [singlish-speaker2202-v1.mp3](singlish-speaker2202-v1.mp3)
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6. [haqkiem-v1.mp3](haqkiem-v1.mp3)
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**Only `singlish-speaker2202` and `haqkiem` had to generate 2 times to get better output that follow exact text input**.
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## Limitation
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1. This model trained on normalized text, so if you have text such as `123`, you have to normalize it first to become `one two three` or `one hundred twenty three` or `satu dua tiga` or `seratus dua puluh tiga`. Feel free to use Malaya for normalization, Malaya support Malay and English normalization, read more at https://github.com/mesolitica/malaya/issues/247#issuecomment-3030313021
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2. The repetitive pronunciation dataset does not consistently use commas for pauses. For example, `A, A, A, A, B, B` in our recordings is spoken as `A A A A B B`. We have no intention to improve it due to cost, but continue finetune using proper dataset should able to solve it.
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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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32807
added_tokens.json
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85
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{%- if tools %}
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{{- '<|im_start|>system\n' }}
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{%- if messages[0].role == 'system' %}
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{{- messages[0].content + '\n\n' }}
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{%- endif %}
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{{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
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{%- for tool in tools %}
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{{- "\n" }}
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{{- tool | tojson }}
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{%- endfor %}
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{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
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{%- else %}
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{%- if messages[0].role == 'system' %}
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{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
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{%- for message in messages[::-1] %}
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{%- set index = (messages|length - 1) - loop.index0 %}
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{%- if ns.multi_step_tool and message.role == "user" and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
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{%- set ns.multi_step_tool = false %}
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{%- set ns.last_query_index = index %}
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{%- endif %}
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{%- endfor %}
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{%- for message in messages %}
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
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{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{%- set content = message.content %}
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{%- set reasoning_content = '' %}
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{%- if message.reasoning_content is defined and message.reasoning_content is not none %}
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{%- set reasoning_content = message.reasoning_content %}
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{%- else %}
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{%- if '</think>' in message.content %}
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{%- set content = message.content.split('</think>')[-1].lstrip('\n') %}
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{%- set reasoning_content = message.content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
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{%- endif %}
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{%- endif %}
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{%- if loop.index0 > ns.last_query_index %}
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{%- if loop.last or (not loop.last and reasoning_content) %}
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{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
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{%- else %}
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{{- '<|im_start|>' + message.role + '\n' + content }}
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{%- endif %}
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{%- else %}
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{{- '<|im_start|>' + message.role + '\n' + content }}
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{%- endif %}
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{%- if message.tool_calls %}
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{%- for tool_call in message.tool_calls %}
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{%- if (loop.first and content) or (not loop.first) %}
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{{- '\n' }}
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{%- endif %}
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{%- if tool_call.function %}
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{%- set tool_call = tool_call.function %}
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{%- endif %}
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{{- '<tool_call>\n{"name": "' }}
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{{- tool_call.name }}
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{{- '", "arguments": ' }}
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{%- if tool_call.arguments is string %}
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{{- tool_call.arguments }}
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{%- else %}
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{{- tool_call.arguments | tojson }}
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{%- endif %}
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{{- '}\n</tool_call>' }}
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{%- endfor %}
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{%- endif %}
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{{- '<|im_end|>\n' }}
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{%- elif message.role == "tool" %}
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{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
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{{- '<|im_start|>user' }}
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{%- endif %}
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{{- '\n<tool_response>\n' }}
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{{- message.content }}
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{{- '\n</tool_response>' }}
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{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
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{{- '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- endfor %}
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{%- if add_generation_prompt %}
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{{- '<|im_start|>assistant\n' }}
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{%- if enable_thinking is defined and enable_thinking is false %}
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{{- '<think>\n\n</think>\n\n' }}
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{%- endif %}
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{%- endif %}
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60
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{
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"architectures": [
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"Qwen3ForCausalLM"
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],
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"attention_bias": false,
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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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"layer_types": [
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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|
"full_attention",
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|
"full_attention",
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"full_attention",
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"full_attention"
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],
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"max_position_embeddings": 32768,
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|
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|
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|
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|
||||||
BIN
singaporean-v1.mp3
Normal file
BIN
singaporean-v1.mp3
Normal file
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BIN
singlish-speaker2050-v1.mp3
Normal file
BIN
singlish-speaker2050-v1.mp3
Normal file
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BIN
singlish-speaker2202-v1.mp3
Normal file
BIN
singlish-speaker2202-v1.mp3
Normal file
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31
special_tokens_map.json
Normal file
31
special_tokens_map.json
Normal file
@@ -0,0 +1,31 @@
|
|||||||
|
{
|
||||||
|
"additional_special_tokens": [
|
||||||
|
"<|im_start|>",
|
||||||
|
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|
||||||
|
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|
||||||
|
"<|object_ref_end|>",
|
||||||
|
"<|box_start|>",
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
],
|
||||||
|
"eos_token": {
|
||||||
|
"content": "<|endoftext|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false
|
||||||
|
},
|
||||||
|
"pad_token": {
|
||||||
|
"content": "<|endoftext|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"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 sha256:5812dc95d7c962d6401e928d7dd5ececa8798f1edab8d2e0fed906b55788546d
|
||||||
|
size 5992799
|
||||||
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