# Speech Synthesis: Magpie TTS Flow Model Overview ## Description: With only a prompt of 5 seconds or less, the Magpie TTS Flow model can analyze a speaker’s voice and replicate voice qualities such as pitch, timbre and speech rate to achieve a speaker similarity of over 70%, and an MOS score of 4.40. Maintaining the original characteristics that capture unique voice audio signature, it can create high-quality audio (speech) when used in combination with a vocoder model like BigVGAN [1]. Magpie TTS Flow [2] is an alignment-aware pre-training method that builds upon E2TTS’s [3] training framework to learn alignment between unit sequences and speech frames. By using de-duplicated units that retain only phonetic content, Magpie TTS Flow effectively learns alignment without relying on a phoneme duration predictor. This allows for direct application to zero-shot voice conversion, where phonetic content can be transferred to the target speaker’s voice without additional fine-tuning. This model is packaged with BigVGAN, a universal vocoder that generalizes well for various out-of-distribution scenarios without fine-tuning. This model is ready for commercial use. **You are responsible for ensuring that your use of NVIDIA AI Foundation Models complies with all applicable laws.** ## License/Terms of Use: [NVIDIA AI Foundation Models Community License Agreement](https://docs.nvidia.com/ai-foundation-models-community-license.pdf) ## References: [1] [BigVGAN: A Universal Neural Vocoder with Large-Scale Training](https://arxiv.org/abs/2206.04658)
[2] [Magpie-TTS-Flow Paper](https://openreview.net/forum?id=e2p1BWR3vq)
[3] [E2 TTS: Embarrassingly Easy Fully Non-Autoregressive Zero-Shot TTS](https://arxiv.org/abs/2406.18009)
[4] [Flow Matching for Generative Modeling](https://arxiv.org/abs/2210.02747)
## Model Architecture: Architecture Type: Flow Matching
Network Architecture: Optimal Transport Conditional Flow Matching (OT-CFM)-based Masked Speech Modeling Flow Matching [4] (FM) is a simulation-free approach for training Continuous Normalizing Flows (CNFs) based on regressing vector fields of fixed conditional probability paths. It is compatible with a general family of Gaussian probability paths for transforming between noise and data samples — which subsumes existing diffusion paths as specific instances. Furthermore, Flow Matching opens the door to training CNFs with other, non-diffusion probability paths. An instance of particular interest is using Optimal Transport (OT) displacement interpolation to define the conditional probability paths. These paths are more efficient than diffusion paths, provide faster training and sampling, and result in better generalization. ## Input: *Input Type:* Text + Audio
*Input Format:*
For Text: Strings (Graphemes in US English)
For Audio: wav file
*Input Parameters:*
For text: One-Dimensional (1D)
For audio prompt: Two-Dimensional (batch x time)
*Other Properties related to Input:*
For Audio: Recommended format for prompt: Mono, PCM-encoded 16 bit audio; sampling rate of 22.05 kHz; between 3 and 5 second duration.
## Output: *Output Type:* Audio
*Output Format:* Audio of shape (batch x time) in wav format
*Output Parameters:* Two-Dimensional (batch x time)
*Other Properties related to Output:* Mono, PCM-encoded 16 bit audio; sampling rate of 22.05 kHz; 20 Second Maximum Length.
**Supported Operating System(s):**
* Linux
## Model Version(s): Magpie-TTS-Flow_v1
## Inference: **Engine:** Triton
**Test Hardware:**
* NVIDIA A100 GPU
* NVIDIA A30 GPU
* NVIDIA A10 GPU
* NVIDIA H100 GPU
* NVIDIA L4 GPU
* NVIDIA L40 GPU
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