hierspeechpp
Maintainer: adirik
4
| Property | Value |
|---|---|
| Run this model | Run on Replicate |
| API spec | View on Replicate |
| Github link | View on Github |
| Paper link | View on Arxiv |
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Model overview
hierspeechpp is a zero-shot speech synthesizer developed by Replicate user adirik. It is a text-to-speech model that can generate speech from text and a target voice, enabling zero-shot speech synthesis. This model is similar to other text-to-speech models like styletts2, voicecraft, and whisperspeech-small, which also focus on generating speech from text or audio.
Model inputs and outputs
hierspeechpp takes in text or audio as input and generates an audio file as output. The model allows you to provide a target voice clip, which it will use to synthesize the output speech. This enables zero-shot speech synthesis, where the model can generate speech in the voice of the target speaker without requiring any additional training data.
Inputs
- input_text: (optional) Text input to the model. If provided, it will be used for the speech content of the output.
- input_sound: (optional) Sound input to the model in .wav format. If provided, it will be used for the speech content of the output.
- target_voice: A voice clip in .wav format containing the speaker to synthesize.
- denoise_ratio: Noise control. 0 means no noise reduction, 1 means maximum noise reduction.
- text_to_vector_temperature: Temperature for text-to-vector model. Larger value corresponds to slightly more random output.
- output_sample_rate: Sample rate of the output audio file.
- scale_output_volume: Scale normalization. If set to true, the output audio will be scaled according to the input sound if provided.
- seed: Random seed to use for reproducibility.
Outputs
- Output: An audio file in .mp3 format containing the synthesized speech.
Capabilities
hierspeechpp can generate high-quality speech by leveraging a target voice clip. It is capable of zero-shot speech synthesis, meaning it can create speech in the voice of the target speaker without any additional training data. This allows for a wide range of applications, such as voice cloning, audiobook narration, and dubbing.
What can I use it for?
You can use hierspeechpp for various speech-related tasks, such as creating custom voice interfaces, generating audio content for podcasts or audiobooks, or even dubbing videos in different languages. The zero-shot nature of the model makes it particularly useful for projects where you need to generate speech in a specific voice without access to a large dataset of that speaker's recordings.
Things to try
One interesting thing to try with hierspeechpp is to experiment with the different input parameters, such as the denoise_ratio and text_to_vector_temperature. By adjusting these settings, you can fine-tune the output to your specific needs, such as reducing background noise or making the speech more natural-sounding. Additionally, you can try using different target voice clips to see how the model adapts to different speakers.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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