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

# LightSpeech MFA SW v1

LightSpeech MFA SW v1 is a text-to-mel-spectrogram model based on the [LightSpeech](https://arxiv.org/abs/2102.04040) architecture. This model was trained from scratch on a real audio dataset. The list of real speakers include:

- sw-KE-OpenBible

We trained an acoustic Swahili model on our speech corpus using [Montreal Forced Aligner v2.0.0](https://github.com/MontrealCorpusTools/Montreal-Forced-Aligner) and used it as the duration extractor. That model, and consequently our model, uses the IPA phone set for Swahili. We used [gruut](https://github.com/rhasspy/gruut) for phonemization purposes. We followed these [steps](https://github.com/TensorSpeech/TensorFlowTTS/tree/master/examples/mfa_extraction) to perform duration extraction.

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/lightspeech-mfa-sw-v1/tensorboard) logged via Tensorboard.

## Model

| Model                   | Config                                                                            | SR (Hz) | Mel range (Hz) | FFT / Hop / Win (pt) | #steps |
| ----------------------- | --------------------------------------------------------------------------------- | ------- | -------------- | -------------------- | ------ |
| `lightspeech-mfa-sw-v1` | [Link](https://huggingface.co/bookbot/lightspeech-mfa-sw-v1/blob/main/config.yml) | 44.1K   | 20-11025       | 2048 / 512 / None    | 200K   |

## Training Procedure

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

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

</details>

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

    model_type: lightspeech
    lightspeech_params:
        dataset: "swahiliipa"
        n_speakers: 1
        encoder_hidden_size: 256
        encoder_num_hidden_layers: 3
        encoder_num_attention_heads: 2
        encoder_attention_head_size: 16
        encoder_intermediate_size: 1024
        encoder_intermediate_kernel_size:
            - 5
            - 25
            - 13
            - 9
        encoder_hidden_act: "mish"
        decoder_hidden_size: 256
        decoder_num_hidden_layers: 3
        decoder_num_attention_heads: 2
        decoder_attention_head_size: 16
        decoder_intermediate_size: 1024
        decoder_intermediate_kernel_size:
            - 17
            - 21
            - 9
            - 13
        decoder_hidden_act: "mish"
        variant_prediction_num_conv_layers: 2
        variant_predictor_filter: 256
        variant_predictor_kernel_size: 3
        variant_predictor_dropout_rate: 0.5
        num_mels: 80
        hidden_dropout_prob: 0.2
        attention_probs_dropout_prob: 0.1
        max_position_embeddings: 2048
        initializer_range: 0.02
        output_attentions: False
        output_hidden_states: False

</details>

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

    batch_size: 8 # Batch size for each GPU with assuming that gradient_accumulation_steps == 1.
    eval_batch_size: 16
    remove_short_samples: true # Whether to remove samples the length of which are less than batch_max_steps.
    allow_cache: true # Whether to allow cache in dataset. If true, it requires cpu memory.
    mel_length_threshold: 32 # remove all targets has mel_length <= 32
    is_shuffle: true # shuffle dataset after each epoch.

</details>

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

    optimizer_params:
        initial_learning_rate: 0.0001
        end_learning_rate: 0.00005
        decay_steps: 150000 # < train_max_steps is recommend.
        warmup_proportion: 0.02
        weight_decay: 0.001

    gradient_accumulation_steps: 2
    var_train_expr:
        null # trainable variable expr (eg. 'embeddings|encoder|decoder' )
        # must separate by |. if var_train_expr is null then we
        # training all variable

</details>

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

    train_max_steps: 200000 # Number of training steps.
    save_interval_steps: 5000 # 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.
    delay_f0_energy_steps: 3 # 2 steps use LR outputs only then 1 steps LR + F0 + Energy.

</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 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")

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

mel, duration_outputs, _ = 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),
)
```

## Disclaimer

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

## Authors

LightSpeech MFA 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