---
library_name: transformers
tags: []
---

# Model Card for Model ID

Finetuned SpeechT5 model for text to speech task in bambara.



## Model Details

### Model Description

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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

- **Developed by:** Danube
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- **Finetuned from model [optional]:** SpeechT5

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## Uses

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## How to Get Started with the Model

Use the code below to get started with the model.

```python

from transformers import SpeechT5Processor,  SpeechT5ForTextToSpeech, Seq2SeqTrainingArguments,SpeechT5HifiGan
from IPython.display import Audio

# Replace specials characters.
replacements = [
    ('ç', 'c'),
    ('ā', 'a'),
    ('à', 'a'),
    ('«', '"'),
    ('ù', 'u'),
    ('€', 'euros'),
    ('ɛ', 'e'),
    ('»', '"'),
    ('ī', 'i'),
    ('+', 'plus'),
    ('…', '...'),
    ('ɔ', 'o'),
    ('è', 'e'),
    ('ḥ', 'h'),
    ('ô', 'o'),
    ('ɲ', 'gn'),
    ('Ç', 'C'),
    ('“', '"'),
    ('Ɲ', 'gn'),
    ('Ŋ', 'n'),
    ('ŋ', 'n'),
    ('Ɔ', 'o'),
    ('Ɛ', 'e'),
    ('é', "e"),
    ('ê', "e"),
    ('ɾ', "r")
]
def replace(your_text):
  for old, new in replacements:
    your_text = your_text.replace(old, new)
  return your_text


your_text = replace(your_text)

 

processor = SpeechT5Processor.from_pretrained("Danube/bambara-tts")
model = SpeechT5ForTextToSpeech.from_pretrained("Danube/bambara-tts")
vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")

inputs = processor(text= your_text, return_tensors="pt")
speaker_embeddings = ... # I will upload the speaker embeddings during the train.
speech = model.generate_speech(inputs["input_ids"], speaker_embeddings, vocoder=vocoder)


Audio(speech, rate = 16000)


```

## Training Details

### Training Data

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#### Training Hyperparameters

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#### Summary



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## Environmental Impact

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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

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