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---

language: sw
license: apache-2.0
tags:
  - tensorflowtts
  - audio
  - text-to-speech
  - text-to-mel
inference: false
datasets:
  - bookbot/sw-TZ-Victoria
  - bookbot/sw-TZ-Victoria-syllables-word
  - bookbot/sw-TZ-Victoria-v2
  - bookbot/sw-TZ-VictoriaNeural-upsampled-48kHz
---


# LightSpeech MFA SW v4

LightSpeech MFA SW v4 is a text-to-mel-spectrogram model based on the [LightSpeech](https://arxiv.org/abs/2102.04040) architecture. This model was fine-tuned from [LightSpeech MFA SW v1](https://huggingface.co./bookbot/lightspeech-mfa-sw-v1) and trained on real and synthetic audio datasets. The list of speakers include:

- sw-TZ-Victoria
- sw-TZ-Victoria-syllables-word
- sw-TZ-Victoria-v2
- sw-TZ-VictoriaNeural-upsampled-48kHz

We trained an acoustic Swahili model on our speech corpus using [Montreal Forced Aligner v3.0.0](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 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/lightspeech-mfa-sw-v4/tensorboard) logged via Tensorboard.

## Model

| Model                   | Config                                                                            | SR (Hz) | Mel range (Hz) | FFT / Hop / Win (pt) | #steps |
| ----------------------- | --------------------------------------------------------------------------------- | ------- | -------------- | -------------------- | ------ |
| `lightspeech-mfa-sw-v4` | [Link](https://huggingface.co./bookbot/lightspeech-mfa-sw-v4/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: 16 # 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: 1

    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-v4")

processor = AutoProcessor.from_pretrained("bookbot/lightspeech-mfa-sw-v4")



text, speaker_name = "Hello World", "sw-TZ-Victoria"

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 v4 was trained and evaluated by [David Samuel Setiawan](https://davidsamuell.github.io/), [Wilson Wongso](https://wilsonwongso.dev/). All computation and development are done on local machines.

## Framework versions

- TensorFlowTTS 1.8
- TensorFlow 2.12.0