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parler_tts_mini_v0.1 is a lightweight text-to-speech (TTS) model from the Parler-TTS project. The model was trained on 10.5K hours of audio data and can generate high-quality, natural sounding speech with features that can be controlled using a simple text prompt. This includes the ability to adjust gender, background noise, speaking rate, pitch, and reverberation. It is the first release model from the Parler-TTS project, which aims to provide the community with TTS training resources and dataset pre-processing code. Model inputs and outputs Inputs Text prompt**: A text description that controls the speech generation, including details about the speaker's voice, speaking style, and audio environment. Outputs Audio waveform**: The generated speech audio in WAV format. Capabilities The parler_tts_mini_v0.1 model can produce highly expressive, natural-sounding speech by conditioning on a text description. It is able to control various speech attributes, allowing users to customize the generated voice and acoustic environment. This makes it suitable for a wide range of text-to-speech applications that require high-quality, controllable speech output. What can I use it for? The parler_tts_mini_v0.1 model can be a valuable tool for creating engaging audio content, such as audiobooks, podcasts, and voice interfaces. Its ability to customize the voice and acoustic environment allows for the creation of unique, personalized audio experiences. Potential use cases include virtual assistants, language learning applications, and audio content creation for e-learning or entertainment. Things to try Some interesting things to try with the parler_tts_mini_v0.1 model include: Experimenting with different text prompts to control the speaker's gender, pitch, speaking rate, and background environment. Generating speech in a variety of languages and styles to explore the model's cross-language and cross-style capabilities. Combining the model with other speech processing tools, such as voice conversion or voice activity detection, to create more advanced audio applications. Evaluating the model's performance on specific use cases or domains to understand its strengths and limitations.

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Updated 5/17/2024