---
license: mit
language:
- zh
base_model:
- Qwen/Qwen2.5-14B-Instruct
pipeline_tag: audio-text-to-text
datasets:
- EastBrook/COIG-Kun-Aug-Audio
---

## Model Details
本模型是语音-文本->文本的多模态模型。基于seamless-m4t-v2-large的音频编码器和Qwen2.5-14B-Instruct文本模型。


训练：  
一阶段使用清洗后的WeNet中文数据（约6000小时）进行ASR任务训练，此阶段除文本模型外全部可训练。  
二阶段基于chatgpt-corpus、moss-003-sft-data等数据集的问题，使用Qwen2.5-72B-Instruct-GPTQ-Int4首先继续生成更多轮次的问题，然后使用Qwen2.5-72B-Instruct-GPTQ-Int4生成多轮问题的答案，
问题使用cosyvoice生成对应音频。生成约620k的多轮语音输入->文本回答数据集。进行语音输入->文本回答的问答任务训练。此阶段除文本模型外全部可训练。
部分数据: https://huggingface.co/datasets/ReopenAI/COIG-Kun-Aug-Audio

优势：训练期间文本模型完全冻结，保留原始能力；seamless-m4t-v2-large编码器平均每秒的音频编码成6-7个token，显著小于whisper的50个token。

### Model Description

<!-- Provide a longer summary of what this model is. -->

- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]

## Uses
```python
import requests
import torch
from torchvision import io
from typing import Dict
from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor, AutoModel, SeamlessM4Tv2Model, AutoModelForCausalLM, AutoConfig


model_path = "EastBrook/Qwen2.5-14B-SeamlessV2"
#model_path = "./"
model = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True, torch_dtype=torch.bfloat16).to("cuda")
processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True)
print("model_path: ", model_path)

messages = [
    {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."},
    {
        "role": "user",
        "content": [
            {"type": "audio"},
            #{"type": "text", "text": "请详细介绍一下强化学习中的GRPO。"},
        ],
    },
]

# Preparation for inference
text = processor.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True
)

import librosa
audios = []
audio_paths = [
    "/mnt/diskhd/Backup/Dataset/WenetSpeech/audio/train/podcast/B00022/X0000005821_5113963_S01270.mp3",
]

for path in audio_paths:
    audio, sr = librosa.load(path, sr=16000)
    audios.append(audio)


inputs = processor(
    text=[text],
    images=None,
    videos=None,
    #audios=None,
    audios=audios,
    padding=True,
    return_tensors="pt",
)
inputs = inputs.to("cuda")

generated_ids = model.generate(**inputs, max_new_tokens=512, do_sample=False)
generated_ids_trimmed = [
    out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print("output_text: ", output_text)
```

### Out-of-Scope Use

模型主要使用中文音频训练，英文能力较弱。