Updated README
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README.md
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inference: false
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datasets:
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- bookbot/sw-TZ-Victoria
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- bookbot/sw-TZ-Victoria-syllables
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- bookbot/sw-TZ-Victoria-v2
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- bookbot/sw-TZ-VictoriaNeural
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---
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# MB-MelGAN HiFi PostNets SW v4
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MB-MelGAN HiFi PostNets SW v4 is a mel-to-wav model based on the [MB-MelGAN](https://arxiv.org/abs/2005.05106) architecture with [HiFi-GAN](https://arxiv.org/abs/2010.05646) discriminator. This model was trained from scratch on trained on real and synthetic audio datasets. Instead of training on ground truth waveform spectrograms, this model was trained on the generated PostNet spectrograms of [LightSpeech MFA SW v4](https://huggingface.co/bookbot/lightspeech-mfa-sw-v4). The list of speakers include:
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- sw-TZ-Victoria
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- sw-TZ-Victoria-syllables
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- sw-TZ-Victoria-v2
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- sw-TZ-VictoriaNeural
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This model was trained using the [TensorFlowTTS](https://github.com/TensorSpeech/TensorFlowTTS) framework. All training was done on a RTX 4090 GPU. All necessary scripts used for training could be found in this [Github Fork](https://github.com/bookbot-hive/TensorFlowTTS), as well as the [Training metrics](https://huggingface.co/bookbot/mb-melgan-hifi-postnets-sw-v4/tensorboard) logged via Tensorboard.
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inference: false
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datasets:
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- bookbot/sw-TZ-Victoria
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- bookbot/sw-TZ-Victoria-syllables-word
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- bookbot/sw-TZ-Victoria-v2
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- bookbot/sw-TZ-VictoriaNeural-upsampled-48kHz
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---
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# MB-MelGAN HiFi PostNets SW v4
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MB-MelGAN HiFi PostNets SW v4 is a mel-to-wav model based on the [MB-MelGAN](https://arxiv.org/abs/2005.05106) architecture with [HiFi-GAN](https://arxiv.org/abs/2010.05646) discriminator. This model was trained from scratch on trained on real and synthetic audio datasets. Instead of training on ground truth waveform spectrograms, this model was trained on the generated PostNet spectrograms of [LightSpeech MFA SW v4](https://huggingface.co/bookbot/lightspeech-mfa-sw-v4). The list of speakers include:
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- sw-TZ-Victoria
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+
- sw-TZ-Victoria-syllables-word
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- sw-TZ-Victoria-v2
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| 24 |
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- sw-TZ-VictoriaNeural-upsampled-48kHz
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This model was trained using the [TensorFlowTTS](https://github.com/TensorSpeech/TensorFlowTTS) framework. All training was done on a RTX 4090 GPU. All necessary scripts used for training could be found in this [Github Fork](https://github.com/bookbot-hive/TensorFlowTTS), as well as the [Training metrics](https://huggingface.co/bookbot/mb-melgan-hifi-postnets-sw-v4/tensorboard) logged via Tensorboard.
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