FunAudioLLM

👉🏻 CosyVoice 👈🏻 CosyVoice 2.0: Demos; Paper; Modelscope; HuggingFace CosyVoice 2.0 has been released! Compared to version 1.0, the new version offers more accurate, more stable, faster, and better speech generation capabilities. Multilingual - Supported Language: Chinese, English, Japanese, Korean, Chinese dialects (Cantonese, Sichuanese, Shanghainese, Tianjinese, Wuhanese, etc.) - Crosslingual & Mixlingual：Support zero-shot voice cloning for cross-lingual and code-switching scenarios. Ultra-Low Latency - Bidirectional Streaming Support: CosyVoice 2.0 integrates offline and streaming modeling technologies. - Rapid First Packet Synthesis: Achieves latency as low as 150ms while maintaining high-quality audio output. High Accuracy - Improved Pronunciation: Reduces pronunciation errors by 30% to 50% compared to CosyVoice 1.0. - Benchmark Achievements: Attains the lowest character error rate on the hard test set of the Seed-TTS evaluation set. Strong Stability - Consistency in Timbre: Ensures reliable voice consistency for zero-shot and cross-language speech synthesis. - Cross-language Synthesis: Marked improvements compared to version 1.0. Natural Experience - Enhanced Prosody and Sound Quality: Improved alignment of synthesized audio, raising MOS evaluation scores from 5.4 to 5.53. - Emotional and Dialectal Flexibility: Now supports more granular emotional controls and accent adjustments. - [x] 25hz cosyvoice base model - [x] 25hz cosyvoice voice conversion model - [x] Repetition Aware Sampling(RAS) inference for llm stability - [x] Streaming inference mode support, including kv cache and sdpa for rtf optimization - [x] Flow matching training support - [x] WeTextProcessing support when ttsfrd is not available - [x] Fastapi server and client - Install Conda: please see https://docs.conda.io/en/latest/miniconda.html - Create Conda env: We strongly recommend that you download our pretrained `CosyVoice2-0.5B` `CosyVoice-300M` `CosyVoice-300M-SFT` `CosyVoice-300M-Instruct` model and `CosyVoice-ttsfrd` resource. Optionally, you can unzip `ttsfrd` resouce and install `ttsfrd` package for better text normalization performance. Notice that this step is not necessary. If you do not install `ttsfrd` package, we will use WeTextProcessing by default. We strongly recommend using `CosyVoice2-0.5B` for better performance. Follow code below for detailed usage of each model. You can use our web demo page to get familiar with CosyVoice quickly. For advanced user, we have provided train and inference scripts in `examples/libritts/cosyvoice/run.sh`. Optionally, if you want service deployment, you can run following steps. You can also scan the QR code to join our official Dingding chat group. 1. We borrowed a lot of code from FunASR. 2. We borrowed a lot of code from FunCodec. 3. We borrowed a lot of code from Matcha-TTS. 4. We borrowed a lot of code from AcademiCodec. 5. We borrowed a lot of code from WeNet. Disclaimer The content provided above is for academic purposes only and is intended to demonstrate technical capabilities. Some examples are sourced from the internet. If any content infringes on your rights, please contact us to request its removal.

👉🏻 CosyVoice 👈🏻 CosyVoice 2.0: Demos; Paper; Modelscope; HuggingFace CosyVoice 2.0 has been released! Compared to version 1.0, the new version offers more accurate, more stable, faster, and better speech generation capabilities. Multilingual - Supported Language: Chinese, English, Japanese, Korean, Chinese dialects (Cantonese, Sichuanese, Shanghainese, Tianjinese, Wuhanese, etc.) - Crosslingual & Mixlingual：Support zero-shot voice cloning for cross-lingual and code-switching scenarios. Ultra-Low Latency - Bidirectional Streaming Support: CosyVoice 2.0 integrates offline and streaming modeling technologies. - Rapid First Packet Synthesis: Achieves latency as low as 150ms while maintaining high-quality audio output. High Accuracy - Improved Pronunciation: Reduces pronunciation errors by 30% to 50% compared to CosyVoice 1.0. - Benchmark Achievements: Attains the lowest character error rate on the hard test set of the Seed-TTS evaluation set. Strong Stability - Consistency in Timbre: Ensures reliable voice consistency for zero-shot and cross-language speech synthesis. - Cross-language Synthesis: Marked improvements compared to version 1.0. Natural Experience - Enhanced Prosody and Sound Quality: Improved alignment of synthesized audio, raising MOS evaluation scores from 5.4 to 5.53. - Emotional and Dialectal Flexibility: Now supports more granular emotional controls and accent adjustments. - [x] 25hz cosyvoice base model - [x] 25hz cosyvoice voice conversion model - [x] Repetition Aware Sampling(RAS) inference for llm stability - [x] Streaming inference mode support, including kv cache and sdpa for rtf optimization - [x] Flow matching training support - [x] WeTextProcessing support when ttsfrd is not available - [x] Fastapi server and client - Install Conda: please see https://docs.conda.io/en/latest/miniconda.html - Create Conda env: We strongly recommend that you download our pretrained `CosyVoice2-0.5B` `CosyVoice-300M` `CosyVoice-300M-SFT` `CosyVoice-300M-Instruct` model and `CosyVoice-ttsfrd` resource. Optionally, you can unzip `ttsfrd` resouce and install `ttsfrd` package for better text normalization performance. Notice that this step is not necessary. If you do not install `ttsfrd` package, we will use WeTextProcessing by default. We strongly recommend using `CosyVoice2-0.5B` for better performance. Follow code below for detailed usage of each model. You can use our web demo page to get familiar with CosyVoice quickly. For advanced user, we have provided train and inference scripts in `examples/libritts/cosyvoice/run.sh`. Optionally, if you want service deployment, you can run following steps. You can also scan the QR code to join our official Dingding chat group. 1. We borrowed a lot of code from FunASR. 2. We borrowed a lot of code from FunCodec. 3. We borrowed a lot of code from Matcha-TTS. 4. We borrowed a lot of code from AcademiCodec. 5. We borrowed a lot of code from WeNet. Disclaimer The content provided above is for academic purposes only and is intended to demonstrate technical capabilities. Some examples are sourced from the internet. If any content infringes on your rights, please contact us to request its removal.

👉🏻 CosyVoice 👈🏻 CosyVoice 2.0: Demos; Paper; Modelscope; HuggingFace CosyVoice 2.0 has been released! Compared to version 1.0, the new version offers more accurate, more stable, faster, and better speech generation capabilities. Multilingual - Supported Language: Chinese, English, Japanese, Korean, Chinese dialects (Cantonese, Sichuanese, Shanghainese, Tianjinese, Wuhanese, etc.) - Crosslingual & Mixlingual：Support zero-shot voice cloning for cross-lingual and code-switching scenarios. Ultra-Low Latency - Bidirectional Streaming Support: CosyVoice 2.0 integrates offline and streaming modeling technologies. - Rapid First Packet Synthesis: Achieves latency as low as 150ms while maintaining high-quality audio output. High Accuracy - Improved Pronunciation: Reduces pronunciation errors by 30% to 50% compared to CosyVoice 1.0. - Benchmark Achievements: Attains the lowest character error rate on the hard test set of the Seed-TTS evaluation set. Strong Stability - Consistency in Timbre: Ensures reliable voice consistency for zero-shot and cross-language speech synthesis. - Cross-language Synthesis: Marked improvements compared to version 1.0. Natural Experience - Enhanced Prosody and Sound Quality: Improved alignment of synthesized audio, raising MOS evaluation scores from 5.4 to 5.53. - Emotional and Dialectal Flexibility: Now supports more granular emotional controls and accent adjustments. - [x] 25hz cosyvoice base model - [x] 25hz cosyvoice voice conversion model - [x] Repetition Aware Sampling(RAS) inference for llm stability - [x] Streaming inference mode support, including kv cache and sdpa for rtf optimization - [x] Flow matching training support - [x] WeTextProcessing support when ttsfrd is not available - [x] Fastapi server and client - Install Conda: please see https://docs.conda.io/en/latest/miniconda.html - Create Conda env: We strongly recommend that you download our pretrained `CosyVoice2-0.5B` `CosyVoice-300M` `CosyVoice-300M-SFT` `CosyVoice-300M-Instruct` model and `CosyVoice-ttsfrd` resource. Optionally, you can unzip `ttsfrd` resouce and install `ttsfrd` package for better text normalization performance. Notice that this step is not necessary. If you do not install `ttsfrd` package, we will use WeTextProcessing by default. We strongly recommend using `CosyVoice2-0.5B` for better performance. Follow code below for detailed usage of each model. You can use our web demo page to get familiar with CosyVoice quickly. For advanced user, we have provided train and inference scripts in `examples/libritts/cosyvoice/run.sh`. Optionally, if you want service deployment, you can run following steps. You can also scan the QR code to join our official Dingding chat group. 1. We borrowed a lot of code from FunASR. 2. We borrowed a lot of code from FunCodec. 3. We borrowed a lot of code from Matcha-TTS. 4. We borrowed a lot of code from AcademiCodec. 5. We borrowed a lot of code from WeNet. Disclaimer The content provided above is for academic purposes only and is intended to demonstrate technical capabilities. Some examples are sourced from the internet. If any content infringes on your rights, please contact us to request its removal.