release v1.1.0

Author: WANDY666

README.md

@@ -2,7 +2,12 @@
 
 # LightTTS
 
-**LightTTS** is a lightweight and high-performance text-to-speech (TTS) inference and service framework based on Python. It is built around the [cosyvoice](https://github.com/FunAudioLLM/CosyVoice) model and based on the [lightllm](https://github.com/ModelTC/lightllm), with optimizations to support fast, scalable, and service-ready TTS deployment.
+[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
+[![Docker](https://img.shields.io/badge/docker-ready-brightgreen.svg)](https://hub.docker.com/r/lighttts/light-tts)
+
+**⚡ Lightning-Fast Text-to-Speech Inference & Service Framework**
+
+**LightTTS** is a lightweight and high-performance text-to-speech (TTS) inference and service framework based on Python. It supports **CosyVoice2** and **CosyVoice3** models, built upon the [CosyVoice](https://github.com/FunAudioLLM/CosyVoice) architecture and [LightLLM](https://github.com/ModelTC/lightllm) framework, with optimizations to support fast, scalable, and service-ready TTS deployment.
 
 ---
 
@@ -19,18 +24,18 @@
 
 ### Installation
 
-- Installing with Docker
+- (Option 1 Recommended) Run with Docker
     ```bash
-    # The easiest way to install Lightllm is by using the official image. You can directly pull and run the official image
-    docker pull lighttts/light-tts:v1.0
+    # The easiest way to install LightTTS is by using the official image. You can directly pull and run the official image
+    docker pull lighttts/light-tts:latest
 
     # Or you can manually build the image
-    docker build -t light-tts:v1.0 .
+    docker build -t light-tts:latest .
 
     # Run the image
-    docker run -it --gpus all -p 8080:8080 --shm-size 4g -v your_local_path:/data/ light-tts:v1.0 /bin/bash
+    docker run -it --gpus all -p 8080:8080 --shm-size 4g -v your_local_path:/data/ light-tts:latest /bin/bash
 
-- Installing from Source
+- (Option 2) Install from Source
 
     ```bash
     # Clone the repo
@@ -41,12 +46,11 @@
     # git submodule update --init --recursive
 
     # (Recommended) Create a new conda environment
-    conda create -n light-tts python=3.10 -y
+    conda create -n light-tts python=3.10
     conda activate light-tts
 
-    # pynini is required by WeTextProcessing, use conda to install it as it can be executed on all platforms.
-    conda install -y -c conda-forge pynini==2.1.5
-    pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/ --trusted-host=mirrors.aliyun.com
+    # Install dependencies (We use the latest torch==2.9.1, but other versions are also compatible)
+    pip install -r requirements.txt
 
     # If you encounter sox compatibility issues
     # ubuntu
@@ -55,23 +59,25 @@
     sudo yum install sox sox-devel
     ```
 
-### Model download
+### Model Download
 
-We now only support CosyVoice2 model.
+We now support CosyVoice2 and CosyVoice3 models.
 
 ```python
-# SDK模型下载
+# ModelScope SDK model download (SDK模型下载)
 from modelscope import snapshot_download
+snapshot_download('FunAudioLLM/Fun-CosyVoice3-0.5B-2512', local_dir='pretrained_models/Fun-CosyVoice3-0.5B')
 snapshot_download('iic/CosyVoice2-0.5B', local_dir='pretrained_models/CosyVoice2-0.5B')
 snapshot_download('iic/CosyVoice-ttsfrd', local_dir='pretrained_models/CosyVoice-ttsfrd')
-```
-```python
-# git模型下载，请确保已安装git lfs
-mkdir -p pretrained_models
-git clone https://www.modelscope.cn/iic/CosyVoice2-0.5B.git pretrained_models/CosyVoice2-0.5B
-git clone https://www.modelscope.cn/iic/CosyVoice-ttsfrd.git pretrained_models/CosyVoice-ttsfrd
+
+# For overseas users, HuggingFace SDK model download
+from huggingface_hub import snapshot_download
+snapshot_download('FunAudioLLM/Fun-CosyVoice3-0.5B-2512', local_dir='pretrained_models/Fun-CosyVoice3-0.5B')
+snapshot_download('FunAudioLLM/CosyVoice2-0.5B', local_dir='pretrained_models/CosyVoice2-0.5B')
+snapshot_download('FunAudioLLM/CosyVoice-ttsfrd', local_dir='pretrained_models/CosyVoice-ttsfrd')
 ```
 
+(We have already installed the ttsfrd package in the docker image. If you are using docker image, you can skip this installation)
 For better text normalization performance, you can optionally install the ttsfrd package and unzip its resources. This step is not required — if skipped, the system will fall back to WeTextProcessing by default.
 
 ```bash
@@ -80,78 +86,109 @@ unzip resource.zip -d .
 pip install ttsfrd_dependency-0.1-py3-none-any.whl
 pip install ttsfrd-0.4.2-cp310-cp310-linux_x86_64.whl
 ```
-📝 This setup instruction is based on the original guide from the [CosyVoice repository](https://github.com/FunAudioLLM/CosyVoice).
 
 ### Start the Model Service
 
+**Note:** It is recommended to enable the `load_trt` parameter for acceleration. The default flow precision is fp16 for CosyVoice2 and fp32 for CosyVoice3.
+
+**For CosyVoice2:**
+
+```bash
+python -m light_tts.server.api_server --model_dir ./pretrained_models/CosyVoice2-0.5B
+```
+
+**For CosyVoice3:**
+
+```bash
+python -m light_tts.server.api_server --model_dir ./pretrained_models/Fun-CosyVoice3-0.5B-2512
+```
+
+**With custom data type** (float32, bfloat16, or float16; default: float16):
+
 ```bash
-# It is recommended to enable the load_trt parameter for acceleration.
-# token2wav: The default is fp16 mode for CosyVoice2 and fp32 mode for CosyVoice3.
-python -m light_tts.server.api_server --model_dir ./pretrained_models/CosyVoice2-0.5B-latest
+# Use float32 for better accuracy or float16 for faster speed
+python -m light_tts.server.api_server --model_dir ./pretrained_models/Fun-CosyVoice3-0.5B-2512 --data_type float32
 ```
 
-- max_total_token_num: llm arg, the total token nums the gpu and model can support, equals = `max_batch * (input_len + output_len)`
-- max_req_total_len: llm arg, the max value for `req_input_len + req_output_len`, 32768 is set here because the `max_position_embeddings` of the llm part is 32768
-- There are many other parameters that can be viewed in `light_tts/server/api_cli.py`
+**Available Parameters:**
 
-Wait for a while, this service will be started. The default startup is localhost:8080.
+The default values are usually the fastest and generally do not need to be adjusted. If you need to customize them, please refer to the following parameter descriptions:
+- `load_trt`: Whether to load the flow_decoder in TensorRT mode (default: True).
+- `data_type`: The data type for LLM inference (default: float16)
+- `load_jit`: Whether to load the flow_encoder in JIT mode (default: False).
+- `max_total_token_num`: LLM arg, total token count the GPU and model can support = `max_batch * (input_len + output_len)` (default: 64 * 1024)
+- `max_req_total_len`: LLM arg, maximum value for `req_input_len + req_output_len` (default: 32768, matches `max_position_embeddings`)
+- `graph_max_len_in_batch`: Maximum sequence length for CUDA graph capture in decoding stage (default: 32768)
+- `graph_max_batch_size`: Maximum batch size for CUDA graph capture in decoding stage (default: 16)
+
+For more parameters, see `light_tts/server/api_cli.py`
+
+Wait for the service to initialize. The default address is `http://localhost:8080`.
 
 ### Request Examples
 
-When your service is started, you can call the service through the http API. We support three modes: non-streaming, streaming and bi-streaming.
-
-- non-streaming and streaming. You can also use `test/test_zero_shot.py`, which can print information such as rtf and ttft.
-
-
-    ```python
-    import requests
-    import time
-    import soundfile as sf
-    import numpy as np
-    import os
-    import threading
-    import json
-
-    url = "http://localhost:8080/inference_zero_shot"
-    path = "cosyvoice/asset/zero_shot_prompt.wav" # wav file path
-    prompt_text = "希望你以后能够做的比我还好呦。"
-    tts_text = "收到好友从远方寄来的生日礼物，那份意外的惊喜与深深的祝福让我心中充满了甜蜜的快乐，笑容如花儿般绽放。"
-    stream = True  # Whether to use streaming inference
-    files = {
-        "prompt_wav": ("sample.wav", open(path, "rb"), "audio/wav")
-    }
-    data = {
-        "tts_text": tts_text,
-        "prompt_text": prompt_text,
-        "stream": stream
-    }
-    response = requests.post(url, files=files, data=data, stream=True)
-    sample_rate = 24000
-
-    audio_data = bytearray()
-    try:
-        for chunk in response.iter_content(chunk_size=4096):
-            if chunk:
-                audio_data.extend(chunk)
-    except Exception as e:
-        print(f"Exception: {e}")
-        print(f"Error: {response.status_code}, {response.text}")
-        return
-    audio_np = np.frombuffer(audio_data, dtype=np.int16)
-    if response.status_code == 200:
-        output_wav = f"./outs/output{'_stream' if stream else ''}_{index}.wav"
-        sf.write(output_wav, audio_np, samplerate=sample_rate, subtype="PCM_16")
-        print(f"saved as {output_wav}")
-    else:
-        print("Error:", response.status_code, response.text)
-    ```
+Once the service is running, you can interact with it through the HTTP API. We support three modes: **non-streaming**, **streaming**, and **bi-streaming**.
+
+- **Non-streaming and Streaming**: Use `test/test_zero_shot.py` for examples, which prints metrics such as RTF (Real-Time Factor) and TTFT (Time To First Token)
+- **Bi-streaming**: Uses WebSocket interface. See usage examples in `test/test_bistream.py`
+
+## 📊 Performance Benchmarks
+
+We have conducted performance benchmarks on different GPU configurations to demonstrate the throughput and latency characteristics of LightTTS in streaming mode. 
+
+Model: `Fun-CosyVoice3-0.5B-2512` datatype: `float16`
+
+### NVIDIA GeForce RTX 4090D
+non-stream: `test/test_zs.py`
+
+|num_workers|cost time 50%|cost time 90%|cost time 99%|rtf 50%|rtf 90%|rtf 99%|avg rtf|total_cost_time|qps|
+|------|------|------|------|------|------|------|------|------|------|
+|1|0.61|1.09|1.51|0.13|0.16|0.22|0.13|33.95|1.47|
+|2|0.8|1.24|1.71|0.15|0.22|0.25|0.16|21.46|2.33|
+|4|1.02|1.88|2.27|0.22|0.29|0.38|0.23|15.31|3.27|
+|8|1.76|2.36|3.48|0.33|0.49|0.62|0.36|12.18|4.1|
+
+stream: `test/test_zs_stream.py`
+
+|num_workers|cost time 50%|cost time 90%|cost time 99%|ttft 50%|ttft 90%|ttft 99%|rtf 50%|rtf 90%|rtf 99%|avg rtf|total_cost_time|qps|
+|------|------|------|------|------|------|------|------|------|------|------|------|------|
+|1|1.01|2.15|2.82|0.33|0.34|0.9|0.21|0.25|0.34|0.22|60.13|0.83|
+|2|1.83|3.56|5.16|0.93|1.53|2.3|0.34|0.63|0.81|0.4|52.47|0.95|
+|4|3.43|5.76|7.31|2.62|4.37|5.8|0.7|1.28|2.16|0.81|48.74|1.03|
+|8|7.27|10.01|10.45|6.4|8.55|9.03|1.28|2.67|3.66|1.57|47.37|1.06|
+
+### NVIDIA GeForce RTX 5090
+non-stream
+
+|num_workers|cost time 50%|cost time 90%|cost time 99%|rtf 50%|rtf 90%|rtf 99%|avg rtf|total_cost_time|qps|
+|------|------|------|------|------|------|------|------|------|------|
+|1|0.51|0.81|1.61|0.11|0.13|0.23|0.11|28.9|1.73|
+|2|0.64|1.1|1.48|0.13|0.16|0.26|0.13|17.54|2.85|
+|4|0.87|1.28|1.68|0.17|0.23|0.36|0.18|11.45|4.37|
+|8|1.32|1.86|2.14|0.25|0.4|0.6|0.29|8.97|5.57|
+
+stream
+
+|num_workers|cost time 50%|cost time 90%|cost time 99%|ttft 50%|ttft 90%|ttft 99%|rtf 50%|rtf 90%|rtf 99%|avg rtf|total_cost_time|qps|
+|------|------|------|------|------|------|------|------|------|------|------|------|------|
+|1|0.76|1.41|2.27|0.28|0.3|0.31|0.16|0.18|0.22|0.16|44.06|1.13|
+|2|1.45|2.34|3.46|0.74|1.28|1.75|0.27|0.45|0.7|0.3|38.82|1.29|
+|4|2.9|4.04|4.7|2.16|3.03|3.4|0.5|1.04|1.51|0.61|37.75|1.32|
+|8|5.78|7.74|8.49|5.01|6.73|7.35|1.03|2.09|2.85|1.22|37.67|1.33|
 
-- bi-streaming. We use the websocket interface implementation, and we can find usage examples in `test/test_bistream.py`.
+**Metrics Explanation:**
+- **num_workers**: Number of concurrent workers
+- **cost time**: Total request processing time in seconds (50th/90th/99th percentile)
+- **ttft**: Time to First Token in seconds (50th/90th/99th percentile)
+- **rtf**: Real-Time Factor (50th/90th/99th percentile)
+- **avg rtf**: Average Real-Time Factor
+- **total_cost_time**: Total benchmark duration in seconds
+- **qps**: Queries Per Second
 
 ## License
 
 This repository is released under the [Apache-2.0](LICENSE) license.
 
 ### Third-Party Code Attribution
 
-This project includes code from [CosyVoice](https://github.com/FunAudioLLM/CosyVoice) (Copyright Alibaba, Inc. and its affiliates), which is also licensed under Apache-2.0. The CosyVoice code is located in the `cosyvoice/` directory and has been integrated and modified as part of Light TTS. See [NOTICE](NOTICE) file for complete attribution details.
+This project includes code from [CosyVoice](https://github.com/FunAudioLLM/CosyVoice) (Copyright Alibaba, Inc. and its affiliates), which is also licensed under Apache-2.0. The CosyVoice code is located in the `cosyvoice/` directory and has been integrated and modified as part of LightTTS. See the [NOTICE](NOTICE) file for complete attribution details.