sanchit-gandhi HF staff commited on
Commit
893b8c6
1 Parent(s): 7f48334

Upload README.md with huggingface_hub

Browse files
Files changed (1) hide show
  1. README.md +99 -0
README.md ADDED
@@ -0,0 +1,99 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+
2
+ ---
3
+ license: cc-by-nc-4.0
4
+ tags:
5
+ - mms
6
+ - vits
7
+ pipeline_tag: text-to-speech
8
+ ---
9
+
10
+ # Massively Multilingual Speech (MMS): Malayalam Text-to-Speech
11
+
12
+ This repository contains the **Malayalam (mal)** language text-to-speech (TTS) model checkpoint.
13
+
14
+ This model is part of Facebook's [Massively Multilingual Speech](https://arxiv.org/abs/2305.13516) project, aiming to
15
+ provide speech technology across a diverse range of languages. You can find more details about the supported languages
16
+ and their ISO 639-3 codes in the [MMS Language Coverage Overview](https://dl.fbaipublicfiles.com/mms/misc/language_coverage_mms.html),
17
+ and see all MMS-TTS checkpoints on the Hugging Face Hub: [facebook/mms-tts](https://huggingface.co/models?sort=trending&search=facebook%2Fmms-tts).
18
+
19
+ MMS-TTS is available in the 🤗 Transformers library from version 4.33 onwards.
20
+
21
+ ## Model Details
22
+
23
+ VITS (**V**ariational **I**nference with adversarial learning for end-to-end **T**ext-to-**S**peech) is an end-to-end
24
+ speech synthesis model that predicts a speech waveform conditional on an input text sequence. It is a conditional variational
25
+ autoencoder (VAE) comprised of a posterior encoder, decoder, and conditional prior.
26
+
27
+ A set of spectrogram-based acoustic features are predicted by the flow-based module, which is formed of a Transformer-based
28
+ text encoder and multiple coupling layers. The spectrogram is decoded using a stack of transposed convolutional layers,
29
+ much in the same style as the HiFi-GAN vocoder. Motivated by the one-to-many nature of the TTS problem, where the same text
30
+ input can be spoken in multiple ways, the model also includes a stochastic duration predictor, which allows the model to
31
+ synthesise speech with different rhythms from the same input text.
32
+
33
+ The model is trained end-to-end with a combination of losses derived from variational lower bound and adversarial training.
34
+ To improve the expressiveness of the model, normalizing flows are applied to the conditional prior distribution. During
35
+ inference, the text encodings are up-sampled based on the duration prediction module, and then mapped into the
36
+ waveform using a cascade of the flow module and HiFi-GAN decoder. Due to the stochastic nature of the duration predictor,
37
+ the model is non-deterministic, and thus requires a fixed seed to generate the same speech waveform.
38
+
39
+ For the MMS project, a separate VITS checkpoint is trained on each langauge.
40
+
41
+ ## Usage
42
+
43
+ MMS-TTS is available in the 🤗 Transformers library from version 4.33 onwards. To use this checkpoint,
44
+ first install the latest version of the library:
45
+
46
+ ```
47
+ pip install --upgrade transformers accelerate
48
+ ```
49
+
50
+ Then, run inference with the following code-snippet:
51
+
52
+ ```python
53
+ from transformers import VitsModel, AutoTokenizer
54
+ import torch
55
+
56
+ model = VitsModel.from_pretrained("facebook/mms-tts-mal")
57
+ tokenizer = AutoTokenizer.from_pretrained("facebook/mms-tts-mal")
58
+
59
+ text = "some example text in the Malayalam language"
60
+ inputs = tokenizer(text, return_tensors="pt")
61
+
62
+ with torch.no_grad():
63
+ output = model(**inputs).waveform
64
+ ```
65
+
66
+ The resulting waveform can be saved as a `.wav` file:
67
+
68
+ ```python
69
+ import scipy
70
+
71
+ scipy.io.wavfile.write("techno.wav", rate=model.config.sampling_rate, data=output)
72
+ ```
73
+
74
+ Or displayed in a Jupyter Notebook / Google Colab:
75
+
76
+ ```python
77
+ from IPython.display import Audio
78
+
79
+ Audio(output, rate=model.config.sampling_rate)
80
+ ```
81
+
82
+
83
+
84
+ ## BibTex citation
85
+
86
+ This model was developed by Vineel Pratap et al. from Meta AI. If you use the model, consider citing the MMS paper:
87
+
88
+ ```
89
+ @article{pratap2023mms,
90
+ title={Scaling Speech Technology to 1,000+ Languages},
91
+ author={Vineel Pratap and Andros Tjandra and Bowen Shi and Paden Tomasello and Arun Babu and Sayani Kundu and Ali Elkahky and Zhaoheng Ni and Apoorv Vyas and Maryam Fazel-Zarandi and Alexei Baevski and Yossi Adi and Xiaohui Zhang and Wei-Ning Hsu and Alexis Conneau and Michael Auli},
92
+ journal={arXiv},
93
+ year={2023}
94
+ }
95
+ ```
96
+
97
+ ## License
98
+
99
+ The model is licensed as **CC-BY-NC 4.0**.