Create README.md

README.md

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+---
+base_model:
+- Qwen/Qwen3-8B
+pipeline_tag: feature-extraction
+---
+
+# MOSS-Speech ☄️
+
+## Overview
+
+MOSS-Speech is an open-source bilingual native speech-to-speech model Without text guidance that supports both Chinese and English.
+Our approach combines a **modality-based layer-splitting architecture** with a **frozen pre-training strategy**, leveraging pretrained text LLMs while extending native speech capabilities. Experiments show state-of-the-art results in spoken question answering and competitive speech-to-speech performance compared to text-guided systems.  
+
+## Highlights
+
+- **True Speech-to-Speech Modeling**: No text guidance required.  
+- **Layer-Splitting Architecture**: Integrates modality-specific layers on top of pretrained text LLM backbones.  
+- **Frozen Pre-Training Strategy**: Preserves LLM reasoning while enhancing speech understanding and generation.  
+- **State-of-the-Art Performance**: Excels in spoken question answering and speech-to-speech tasks.  
+- **Expressive & Efficient**: Maintains paralinguistic cues often lost in cascaded pipelines, such as tone, emotion, and prosody.  
+
+
+```python
+"""MossSpeech inference demo aligned with Hugging Face Transformers guidelines."""
+import os
+from dataclasses import astuple
+
+import torch
+import torchaudio
+
+from transformers import (
+    AutoModel,
+    AutoProcessor,
+    GenerationConfig,
+    StoppingCriteria,
+    StoppingCriteriaList,
+)
+
+
+prompt = "Hello!"
+prompt_audio = "<your path to prompt>
+model_path = "fnlp/MOSS-Speech"
+codec_path = "fnlp/MOSS-Speech-Codec"
+output_path = "outputs"
+output_modality = "audio"  # or text
+
+generation_config = GenerationConfig(
+    temperature=0.7,
+    top_p=0.95,
+    top_k=20,
+    repetition_penalty=1.0,
+    max_new_tokens=1000,
+    min_new_tokens=10,
+    do_sample=True,
+    use_cache=True,
+)
+
+
+class StopOnToken(StoppingCriteria):
+    """Stop generation once the final token equals the provided stop ID."""
+
+    def __init__(self, stop_id: int) -> None:
+        super().__init__()
+        self.stop_id = stop_id
+
+    def __call__(self, input_ids: torch.LongTensor, scores) -> bool:  # type: ignore[override]
+        return input_ids[0, -1].item() == self.stop_id
+
+
+def prepare_stopping_criteria(processor):
+    tokenizer = processor.tokenizer
+    stop_tokens = [
+        tokenizer.pad_token_id,
+        tokenizer.convert_tokens_to_ids("<|im_end|>"),
+    ]
+    return StoppingCriteriaList([StopOnToken(token_id) for token_id in stop_tokens])
+
+
+messages = [
+    [
+        {
+            "role": "system",
+            "content": "You are a helpful voice assistant. Answer the user's questions with spoken responses."},
+            # "content": "You are a helpful assistant. Answer the user's questions with text."}, # if output_modality = "text"
+        {
+            "role": "user",
+            "content": prompt
+        }
+    ]
+]
+
+
+processor = AutoProcessor.from_pretrained(model_path, codec_path=codec_path, device="cuda", trust_remote_code=True)
+stopping_criteria = prepare_stopping_criteria(processor)
+encoded_inputs = processor(messages, output_modality)
+
+model = AutoModel.from_pretrained(model_path, trust_remote_code=True, device_map="cuda").eval()
+
+with torch.inference_mode():
+    token_ids = model.generate(
+        input_ids=encoded_inputs["input_ids"].to("cuda"),
+        attention_mask=encoded_inputs["attention_mask"].to("cuda"),
+        generation_config=generation_config,
+        stopping_criteria=stopping_criteria,
+    )
+
+results = processor.decode(token_ids, output_modality, decoder_audio_prompt_path=prompt_audio)
+
+os.makedirs(output_path, exist_ok=True)
+for index, (result, modality) in enumerate(zip(results, output_modality)):
+    audio, text, sample_rate = astuple(result)
+    if modality == "audio":
+        torchaudio.save(f"{output_path}/audio_{index}.wav", audio, sample_rate)
+    else:
+        print(text)
+```