初始化项目，由ModelHub XC社区提供模型

Model: Scicom-intl/Multilingual-TTS-1.7B-Base
Source: Original Platform

.gitattributes

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+*.ckpt filter=lfs diff=lfs merge=lfs -text
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+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+library_name: transformers
+datasets:
+- malaysia-ai/Multilingual-TTS
+- Scicom-intl/Emilia-YODAS-Voice-Conversion
+- Scicom-intl/Malaysian-Emilia
+base_model:
+- Qwen/Qwen3-1.7B-Base
+new_version: Scicom-intl/Multilingual-TTS-1.7B-Base
+language:
+- en
+- ms
+- zh
+- ta
+---
+
+# Multilingual-TTS-1.7B-Base
+
+Continue pretraining [Qwen/Qwen3-1.7B-Base](https://huggingface.co/Qwen/Qwen3-1.7B-Base) on Multilingual Voice Conversion and TTS.
+
+1. Use [neucodec](https://huggingface.co/neuphonic/neucodec) as speech detokenizer, 50 TPS, output in 24k sample rate.
+2. Multi-speaker multilingual Voice Cloning, **up to 35.88B tokens**.
+3. Multi-speaker multilingual TTS more than 150 languages, **up to 25.35B tokens**.
+4. Flash Attention 3 10k context length varlen multipacking.
+5. Mixed precision FP32-BF16.
+6. MuonAdamW optimizer.
+
+## Benchmark
+
+<img width="50%" src="https://raw.githubusercontent.com/Scicom-AI-Enterprise-Organization/Multilingual-TTS/refs/heads/main/vc-evaluation/scatter_results.png">
+
+<img src="https://raw.githubusercontent.com/Scicom-AI-Enterprise-Organization/Multilingual-TTS/refs/heads/main/vc-evaluation/benchmark_results.png">
+
+All steps to reproduce at https://github.com/Scicom-AI-Enterprise-Organization/Multilingual-TTS/tree/main/vc-evaluation
+
+## How to
+
+First load Neucodec,
+
+```python
+from neucodec import NeuCodec
+
+codec = NeuCodec.from_pretrained("neuphonic/neucodec")
+_ = codec.eval().to('cuda')
+```
+
+### TTS
+
+You can use any speaker name available at https://huggingface.co/datasets/malaysia-ai/Multilingual-TTS
+
+```python
+import re
+import soundfile as sf
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model = AutoModelForCausalLM.from_pretrained('Scicom-intl/Multilingual-TTS-1.7B-Base')
+tokenizer = AutoTokenizer.from_pretrained('Scicom-intl/Multilingual-TTS-1.7B-Base')
+
+speaker = 'husein'
+text = "Hi nama saya Husein, I am so cute, 我喜欢吃鸡饭, boire du thé glacé, ולהירגע על החוף, وأحب أن أتعرض لبعض أشعة الشمس."
+prompt = f"<|im_start|>{speaker}: {text}<|speech_start|>"
+
+inputs = tokenizer(prompt,return_tensors="pt", add_special_tokens=True).to(model.device)
+
+with torch.no_grad():
+    outputs = model.generate(
+        **inputs,
+        max_new_tokens=2048,
+        do_sample=True,
+        temperature=0.8,
+        repetition_penalty=1.15,
+    )
+
+generated_text = tokenizer.decode(outputs[0], skip_special_tokens=False)
+audio_tokens = re.findall(r'<\|s_(\d+)\|>', generated_text.split('<|speech_start|>')[1])
+audio_tokens = [int(token) for token in audio_tokens]
+audio_codes = torch.tensor(audio_tokens)[None, None]
+
+with torch.no_grad():
+    audio_waveform = codec.decode_code(audio_codes.cuda())
+
+sf.write('husein-ms-en-zh-fr-he-ar.mp3', audio_waveform[0, 0].cpu().numpy(), 24000)
+```
+
+You can check the audio at [husein-ms-en-zh-fr-he-ar.mp3](husein-ms-en-zh-fr-he-ar.mp3).
+
+### Voice Cloning
+
+Jenny from https://huggingface.co/datasets/reach-vb/jenny_tts_dataset
+
+```python
+y, sr = librosa.load('jenny.wav', sr = 16000)
+with torch.no_grad():
+    codes = codec.encode_code(torch.tensor(y)[None, None])
+tokens = ''.join([f'<|s_{i}|>' for i in codes[0, 0]])
+prompt = f"<|im_start|>I wonder if I shall ever be happy enough to have real lace on my clothes and bows on my caps.<|speech_start|>{tokens}<|im_end|><|im_start|>Ye encik, apa yang saya boleh tolong? வணக்கம், நான் உங்களுக்கு என்ன உதவ வேண்டும்? Quieres pedir algo de comida? それとも飲み物も欲しいですか？<|speech_start|>"
+
+inputs = tokenizer(prompt,return_tensors="pt", add_special_tokens=True).to(model.device)
+
+with torch.no_grad():
+    outputs = model.generate(
+        **inputs,
+        max_new_tokens=2048,
+        do_sample=True,
+        temperature=0.8,
+        repetition_penalty=1.15,
+    )
+generated_text = tokenizer.decode(outputs[0], skip_special_tokens=False)
+audio_tokens = re.findall(r'<\|s_(\d+)\|>', generated_text.split('<|speech_start|>')[-1])
+audio_tokens = [int(token) for token in audio_tokens]
+audio_codes = torch.tensor(audio_tokens)[None, None]
+
+with torch.no_grad():
+    audio_waveform = codec.decode_code(audio_codes.cuda())
+    
+sf.write('vc-jenny-ms-ta-es-ja.mp3', audio_waveform[0, 0].cpu().numpy(), 24000)
+```
+
+You can check the audio at [vc-jenny-ms-ta-es-ja.mp3](vc-jenny-ms-ta-es-ja.mp3).
+
+## Optimize Inference
+
+For better concurrency, you can use https://github.com/Scicom-AI-Enterprise-Organization/TTS-API-Neucodec
+
+## Source code
+
+All ablations and steps to reproduce at https://github.com/Scicom-AI-Enterprise-Organization/Multilingual-TTS
+
+## Acknowledgement
+
+Special thanks to https://www.scitix.ai/ for H100 Node!

chat_template.jinja

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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0].role == 'system' %}
+        {{- messages[0].content + '\n\n' }}
+    {%- endif %}
+    {{- "# 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>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\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" }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
+{%- for message in messages[::-1] %}
+    {%- set index = (messages|length - 1) - loop.index0 %}
+    {%- if ns.multi_step_tool and message.role == "user" and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
+        {%- set ns.multi_step_tool = false %}
+        {%- set ns.last_query_index = index %}
+    {%- endif %}
+{%- endfor %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {%- set content = message.content %}
+        {%- set reasoning_content = '' %}
+        {%- if message.reasoning_content is defined and message.reasoning_content is not none %}
+            {%- set reasoning_content = message.reasoning_content %}
+        {%- else %}
+            {%- if '</think>' in message.content %}
+                {%- set content = message.content.split('</think>')[-1].lstrip('\n') %}
+                {%- set reasoning_content = message.content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
+            {%- endif %}
+        {%- endif %}
+        {%- if loop.index0 > ns.last_query_index %}
+            {%- if loop.last or (not loop.last and reasoning_content) %}
+                {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
+            {%- else %}
+                {{- '<|im_start|>' + message.role + '\n' + content }}
+            {%- endif %}
+        {%- else %}
+            {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- endif %}
+        {%- if message.tool_calls %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if (loop.first and content) or (not loop.first) %}
+                    {{- '\n' }}
+                {%- endif %}
+                {%- if tool_call.function %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {{- '<tool_call>\n{"name": "' }}
+                {{- tool_call.name }}
+                {{- '", "arguments": ' }}
+                {%- if tool_call.arguments is string %}
+                    {{- tool_call.arguments }}
+                {%- else %}
+                    {{- tool_call.arguments | tojson }}
+                {%- endif %}
+                {{- '}\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+    {%- if enable_thinking is defined and enable_thinking is false %}
+        {{- '<think>\n\n</think>\n\n' }}
+    {%- endif %}
+{%- endif %}

config.json

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+{
+  "architectures": [
+    "Qwen3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "dtype": "float32",
+  "eos_token_id": 151643,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 6144,
+  "layer_types": [
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 32768,
+  "max_window_layers": 28,
+  "model_type": "qwen3",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 8,
+  "pad_token_id": 151643,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000,
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "transformers_version": "4.57.6",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 217208
+}

generation_config.json

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+{
+  "eos_token_id": 151645,
+  "max_new_tokens": 2048,
+  "pad_token_id": 151643,
+  "transformers_version": "4.57.6"
+}

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special_tokens_map.json

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+{
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+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
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+    "normalized": false,
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+    "single_word": false
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.json

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tokenizer_config.json

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