---
language: sw
license: cc-by-sa-4.0
tags:
  - tensorflowtts
  - audio
  - text-to-speech
  - mel-to-wav
inference: false
datasets:
  - bookbot/OpenBible_Swahili
---

# MB-MelGAN HiFi PostNets SW v1

MB-MelGAN HiFi PostNets SW v1 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 a synthetic audio dataset. Instead of training on ground truth waveform spectrograms, this model was trained on the generated PostNet spectrograms of [LightSpeech MFA SW v1](https://huggingface.co/bookbot/lightspeech-mfa-sw-v1). The list of real speakers include:

- sw-KE-OpenBible

This model was trained using the [TensorFlowTTS](https://github.com/TensorSpeech/TensorFlowTTS) framework. All training was done on a Scaleway RENDER-S VM with a Tesla P100 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-v1/tensorboard) logged via Tensorboard.

## Model

| Model                           | Config                                                                                    | SR (Hz) | Mel range (Hz) | FFT / Hop / Win (pt) | #steps |
| ------------------------------- | ----------------------------------------------------------------------------------------- | ------- | -------------- | -------------------- | ------ |
| `mb-melgan-hifi-postnets-sw-v1` | [Link](https://huggingface.co/bookbot/mb-melgan-hifi-postnets-sw-v1/blob/main/config.yml) | 44.1K   | 20-11025       | 2048 / 512 / None    | 1M     |

## Training Procedure

<details>
  <summary>Feature Extraction Setting</summary>

    sampling_rate: 44100
    hop_size: 512 # Hop size.
    format: "npy"

</details>

<details>
  <summary>Generator Network Architecture Setting</summary>

    model_type: "multiband_melgan_generator"

    multiband_melgan_generator_params:
        out_channels: 4 # Number of output channels (number of subbands).
        kernel_size: 7 # Kernel size of initial and final conv layers.
        filters: 384 # Initial number of channels for conv layers.
        upsample_scales: [8, 4, 4] # List of Upsampling scales.
        stack_kernel_size: 3 # Kernel size of dilated conv layers in residual stack.
        stacks: 4 # Number of stacks in a single residual stack module.
        is_weight_norm: false # Use weight-norm or not.

</details>

<details>
  <summary>Discriminator Network Architecture Setting</summary>

    multiband_melgan_discriminator_params:
        out_channels: 1 # Number of output channels.
        scales: 3 # Number of multi-scales.
        downsample_pooling: "AveragePooling1D" # Pooling type for the input downsampling.
        downsample_pooling_params: # Parameters of the above pooling function.
            pool_size: 4
            strides: 2
        kernel_sizes: [5, 3] # List of kernel size.
        filters: 16 # Number of channels of the initial conv layer.
        max_downsample_filters: 512 # Maximum number of channels of downsampling layers.
        downsample_scales: [4, 4, 4] # List of downsampling scales.
        nonlinear_activation: "LeakyReLU" # Nonlinear activation function.
        nonlinear_activation_params: # Parameters of nonlinear activation function.
            alpha: 0.2
        is_weight_norm: false # Use weight-norm or not.

    hifigan_discriminator_params:
        out_channels: 1 # Number of output channels (number of subbands).
        period_scales: [3, 5, 7, 11, 17, 23, 37] # List of period scales.
        n_layers: 5 # Number of layer of each period discriminator.
        kernel_size: 5 # Kernel size.
        strides: 3 # Strides
        filters: 8 # In Conv filters of each period discriminator
        filter_scales: 4 # Filter scales.
        max_filters: 512 # maximum filters of period discriminator's conv.
        is_weight_norm: false # Use weight-norm or not.

</details>

<details>
  <summary>STFT Loss Setting</summary>

    stft_loss_params:
        fft_lengths: [1024, 2048, 512] # List of FFT size for STFT-based loss.
        frame_steps: [120, 240, 50] # List of hop size for STFT-based loss
        frame_lengths: [600, 1200, 240] # List of window length for STFT-based loss.

    subband_stft_loss_params:
        fft_lengths: [384, 683, 171] # List of FFT size for STFT-based loss.
        frame_steps: [30, 60, 10] # List of hop size for STFT-based loss
        frame_lengths: [150, 300, 60] # List of window length for STFT-based loss.

</details>

<details>
  <summary>Adversarial Loss Setting</summary>

    lambda_feat_match: 10.0 # Loss balancing coefficient for feature matching loss
    lambda_adv: 2.5 # Loss balancing coefficient for adversarial loss.

</details>

<details>
  <summary>Data Loader Setting</summary>

    batch_size: 32 # Batch size for each GPU with assuming that gradient_accumulation_steps == 1.
    eval_batch_size: 16
    batch_max_steps: 8192 # Length of each audio in batch for training. Make sure dividable by hop_size.
    batch_max_steps_valid: 8192 # Length of each audio for validation. Make sure dividable by hope_size.
    remove_short_samples: true # Whether to remove samples the length of which are less than batch_max_steps.
    allow_cache: false # Whether to allow cache in dataset. If true, it requires cpu memory.
    is_shuffle: false # shuffle dataset after each epoch.

</details>

<details>
  <summary>Optimizer & Scheduler Setting</summary>

    generator_optimizer_params:
        lr_fn: "PiecewiseConstantDecay"
        lr_params:
            boundaries: [100000, 200000, 300000, 400000, 500000, 600000, 700000]
            values:
                [
                    0.0005,
                    0.0005,
                    0.00025,
                    0.000125,
                    0.0000625,
                    0.00003125,
                    0.000015625,
                    0.000001,
                ]
        amsgrad: false

    discriminator_optimizer_params:
        lr_fn: "PiecewiseConstantDecay"
        lr_params:
            boundaries: [100000, 200000, 300000, 400000, 500000]
            values: [0.00025, 0.000125, 0.0000625, 0.00003125, 0.000015625, 0.000001]
        amsgrad: false

    gradient_accumulation_steps: 1

</details>

<details>
  <summary>Interval Setting</summary>

    discriminator_train_start_steps: 200000 # steps begin training discriminator
    train_max_steps: 1000000 # Number of training steps.
    save_interval_steps: 20000 # Interval steps to save checkpoint.
    eval_interval_steps: 5000 # Interval steps to evaluate the network.
    log_interval_steps: 200 # Interval steps to record the training log.

</details>

<details>
  <summary>Other Setting</summary>

    num_save_intermediate_results: 1 # Number of batch to be saved as intermediate results.

</details>

## How to Use

```py
import soundfile as sf
import tensorflow as tf
from tensorflow_tts.inference import TFAutoModel, AutoProcessor

lightspeech = TFAutoModel.from_pretrained("bookbot/lightspeech-mfa-sw-v1")
processor = AutoProcessor.from_pretrained("bookbot/lightspeech-mfa-sw-v1")
mb_melgan = TFAutoModel.from_pretrained("bookbot/mb-melgan-hifi-postnets-sw-v1")

text, speaker_name = "Hello World.", "sw-KE-OpenBible"
input_ids = processor.text_to_sequence(text)

mel, _, _ = lightspeech.inference(
    input_ids=tf.expand_dims(tf.convert_to_tensor(input_ids, dtype=tf.int32), 0),
    speaker_ids=tf.convert_to_tensor(
        [processor.speakers_map[speaker_name]], dtype=tf.int32
    ),
    speed_ratios=tf.convert_to_tensor([1.0], dtype=tf.float32),
    f0_ratios=tf.convert_to_tensor([1.0], dtype=tf.float32),
    energy_ratios=tf.convert_to_tensor([1.0], dtype=tf.float32),
)

audio = mb_melgan.inference(mel)[0, :, 0]
sf.write("./audio.wav", audio, 44100, "PCM_16")
```

## Disclaimer

Do consider the biases which came from pre-training datasets that may be carried over into the results of this model.

## Authors

MB-MelGAN HiFi PostNets SW v1 was trained and evaluated by [David Samuel Setiawan](https://davidsamuell.github.io/), [Wilson Wongso](https://wilsonwongso.dev/). All computation and development are done on Scaleway.

## Framework versions

- TensorFlowTTS 1.8
- TensorFlow 2.7.0