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GitHub Stars: 4.99K
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Release Date: Jun 12, 2022
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License

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Python Versions Compatibility: >=3.7

Age: 3 years
Latest Release: 2 years ago
Dependencies: 3 Direct / 12 Total
Versions: 4
Maintainers: 1
Wheels: OS Independent

Readme

header

Silero Models

Silero Models

Silero Models: pre-trained enterprise-grade STT / TTS models and benchmarks.

Enterprise-grade STT made refreshingly simple (seriously, see benchmarks). We provide quality comparable to Google's STT (and sometimes even better) and we are not Google.

As a bonus:

No Kaldi;
No compilation;
No 20-step instructions;

Also we have published TTS models that satisfy the following criteria:

One-line usage;
A large library of voices;
A fully end-to-end pipeline;
Naturally sounding speech;
No GPU or training required;
Minimalism and lack of dependencies;
Faster than real-time on one CPU thread (!!!);
Support for 16kHz and 8kHz out of the box;

Also we have published a model for text repunctuation and recapitalization that:

Inserts capital letters and basic punctuation marks (dot, comma, hyphen, question mark, exclamation mark, dash for Russian);
Works for 4 languages (Russian, English, German, Spanish) and can be extended;
By design is domain agnostic and is not based on any hard-coded rules;
Has non-trivial metrics and succeeds in the task of improving text readability;

Installation and Basics

You can basically use our models in 3 flavours:

Via PyTorch Hub: torch.hub.load();
Via pip: pip install silero and then import silero;
Via caching the required models and utils manually and modifying if necessary;

Models are downloaded on demand both by pip and PyTorch Hub. If you need caching, do it manually or via invoking a necessary model once (it will be downloaded to a cache folder). Please see these docs for more information.

PyTorch Hub and pip package are based on the same code. Hence all examples, historically based on torch.hub.load can be used with a pip-package via this basic change:

# before
torch.hub.load(repo_or_dir='snakers4/silero-models',
               model='silero_stt',  # or silero_tts or silero_te
               **kwargs)

# after
from silero import silero_stt, silero_tts, silero_te
silero_stt(**kwargs)

Speech-To-Text

All of the provided models are listed in the models.yml file. Any meta-data and newer versions will be added there.

Screenshot_1

Currently we provide the following checkpoints:

	PyTorch	ONNX	Quantization	Quality
English (`en_v6`)	✔️	✔️	✔️	link
English (`en_v5`)	✔️	✔️	✔️	link
German (`de_v4`)	✔️	✔️	⌛	link
English (`en_v3`)	✔️	✔️	✔️	link
German (`de_v3`)	✔️	⌛	⌛	link
German (`de_v1`)	✔️	✔️	⌛	link
Spanish (`es_v1`)	✔️	✔️	⌛	link
Ukrainian (`ua_v3`)	✔️	✔️	✔️	N/A

Model flavours:

	jit	jit	jit	jit	jit_q	jit_q	onnx	onnx	onnx	onnx
	xsmall	small	large	xlarge	xsmall	small	xsmall	small	large	xlarge
English `en_v6`		✔️		✔️		✔️		✔️		✔️
English `en_v5`		✔️		✔️		✔️		✔️		✔️
English `en_v4_0`			✔️						✔️
English `en_v3`	✔️	✔️	✔️		✔️	✔️	✔️	✔️	✔️
German `de_v4`			✔️						✔️
German `de_v3`			✔️
German `de_v1`		✔️					✔️
Spanish `es_v1`		✔️					✔️
Ukrainian `ua_v3`		✔️			✔️		✔️

Dependencies

All examples:
- torch, 1.8+ (used to clone the repo in tf and onnx examples), breaking changes for version older than 1.6
- torchaudio, latest version bound to PyTorch should work
- omegaconf, latest just should work
Additional for ONNX examples:
- onnx, latest just should work
- onnxruntime, latest just should work
Additional for TensorFlow examples:
- tensorflow, latest just should work
- tensorflow_hub, latest just should work

Please see the provided Colab for details for each example below. All examples are maintained to work with the latest major packaged versions of the installed libraries.

PyTorch

import torch
import zipfile
import torchaudio
from glob import glob

device = torch.device('cpu')  # gpu also works, but our models are fast enough for CPU
model, decoder, utils = torch.hub.load(repo_or_dir='snakers4/silero-models',
                                       model='silero_stt',
                                       language='en', # also available 'de', 'es'
                                       device=device)
(read_batch, split_into_batches,
 read_audio, prepare_model_input) = utils  # see function signature for details

# download a single file, any format compatible with TorchAudio
torch.hub.download_url_to_file('https://opus-codec.org/static/examples/samples/speech_orig.wav',
                               dst ='speech_orig.wav', progress=True)
test_files = glob('speech_orig.wav')
batches = split_into_batches(test_files, batch_size=10)
input = prepare_model_input(read_batch(batches[0]),
                            device=device)

output = model(input)
for example in output:
    print(decoder(example.cpu()))

ONNX

You can run our model everywhere, where you can import the ONNX model or run ONNX runtime.

import onnx
import torch
import onnxruntime
from omegaconf import OmegaConf

language = 'en' # also available 'de', 'es'

# load provided utils
_, decoder, utils = torch.hub.load(repo_or_dir='snakers4/silero-models', model='silero_stt', language=language)
(read_batch, split_into_batches,
 read_audio, prepare_model_input) = utils

# see available models
torch.hub.download_url_to_file('https://raw.githubusercontent.com/snakers4/silero-models/master/models.yml', 'models.yml')
models = OmegaConf.load('models.yml')
available_languages = list(models.stt_models.keys())
assert language in available_languages

# load the actual ONNX model
torch.hub.download_url_to_file(models.stt_models.en.latest.onnx, 'model.onnx', progress=True)
onnx_model = onnx.load('model.onnx')
onnx.checker.check_model(onnx_model)
ort_session = onnxruntime.InferenceSession('model.onnx')

# download a single file, any format compatible with TorchAudio
torch.hub.download_url_to_file('https://opus-codec.org/static/examples/samples/speech_orig.wav', dst ='speech_orig.wav', progress=True)
test_files = ['speech_orig.wav']
batches = split_into_batches(test_files, batch_size=10)
input = prepare_model_input(read_batch(batches[0]))

# actual onnx inference and decoding
onnx_input = input.detach().cpu().numpy()
ort_inputs = {'input': onnx_input}
ort_outs = ort_session.run(None, ort_inputs)
decoded = decoder(torch.Tensor(ort_outs[0])[0])
print(decoded)

TensorFlow

SavedModel example

import os
import torch
import subprocess
import tensorflow as tf
import tensorflow_hub as tf_hub
from omegaconf import OmegaConf

language = 'en' # also available 'de', 'es'

# load provided utils using torch.hub for brevity
_, decoder, utils = torch.hub.load(repo_or_dir='snakers4/silero-models', model='silero_stt', language=language)
(read_batch, split_into_batches,
 read_audio, prepare_model_input) = utils

# see available models
torch.hub.download_url_to_file('https://raw.githubusercontent.com/snakers4/silero-models/master/models.yml', 'models.yml')
models = OmegaConf.load('models.yml')
available_languages = list(models.stt_models.keys())
assert language in available_languages

# load the actual tf model
torch.hub.download_url_to_file(models.stt_models.en.latest.tf, 'tf_model.tar.gz')
subprocess.run('rm -rf tf_model && mkdir tf_model && tar xzfv tf_model.tar.gz -C tf_model',  shell=True, check=True)
tf_model = tf.saved_model.load('tf_model')

# download a single file, any format compatible with TorchAudio
torch.hub.download_url_to_file('https://opus-codec.org/static/examples/samples/speech_orig.wav', dst ='speech_orig.wav', progress=True)
test_files = ['speech_orig.wav']
batches = split_into_batches(test_files, batch_size=10)
input = prepare_model_input(read_batch(batches[0]))

# tf inference
res = tf_model.signatures["serving_default"](tf.constant(input.numpy()))['output_0']
print(decoder(torch.Tensor(res.numpy())[0]))

Text-To-Speech

Models and Speakers

All of the provided models are listed in the models.yml file. Any meta-data and newer versions will be added there.

V3

V3 models support SSML. Also see Colab examples for main SSML tag usage.

ID	Speakers	Auto-stress	Language	SR
`v3_1_ru`	`aidar`, `baya`, `kseniya`, `xenia`, `eugene`, `random`	yes	`ru` (Russian)	`8000`, `24000`, `48000`
`v3_en`	`en_0`, `en_1`, ..., `en_117`, `random`	no	`en` (English)	`8000`, `24000`, `48000`
`v3_en_indic`	`tamil_female`, ..., `assamese_male`, `random`	no	`en` (English)	`8000`, `24000`, `48000`
`v3_de`	`eva_k`, ..., `karlsson`, `random`	no	`de` (German)	`8000`, `24000`, `48000`
`v3_es`	`es_0`, `es_1`, `es_2`, `random`	no	`es` (Spanish)	`8000`, `24000`, `48000`
`v3_fr`	`fr_0`, ..., `fr_5`, `random`	no	`fr` (French)	`8000`, `24000`, `48000`
`v3_tt`	`dilyara`	no	`tt` (Tatar)	`8000`, `24000`, `48000`
`v3_ua`	`mykyta`, `random`	no	`ua` (Ukrainian)	`8000`, `24000`, `48000`
`v3_uz`	`dilnavoz`	no	`uz` (Uzbek)	`8000`, `24000`, `48000`
`v3_xal`	`erdni`, `delghir`, `random`	no	`xal` (Kalmyk)	`8000`, `24000`, `48000`
`v3_indic`	`hindi_male`, `hindi_female`, ..., `random`	no	`indic` (Hindi, Telugu, ...)	`8000`, `24000`, `48000`
`ru_v3`	`aidar`, `baya`, `kseniya`, `xenia`, `random`	yes	`ru` (Russian)	`8000`, `24000`, `48000`

Dependencies

Basic dependencies for colab examples:

torch, 1.10+;
torchaudio, latest version bound to PyTorch should work (required only because models are hosted together with STT, not required for work);
omegaconf, latest (can be removed as well, if you do not load all of the configs);

PyTorch

# V3
import torch

language = 'ru'
model_id = 'v3_1_ru'
sample_rate = 48000
speaker = 'xenia'
device = torch.device('cpu')

model, example_text = torch.hub.load(repo_or_dir='snakers4/silero-models',
                                     model='silero_tts',
                                     language=language,
                                     speaker=model_id)
model.to(device)  # gpu or cpu

audio = model.apply_tts(text=example_text,
                        speaker=speaker,
                        sample_rate=sample_rate)

Standalone Use

Standalone usage just requires PyTorch 1.10+ and python standard library;
Please see the detailed examples in Colab;

# V3
import os
import torch

device = torch.device('cpu')
torch.set_num_threads(4)
local_file = 'model.pt'

if not os.path.isfile(local_file):
    torch.hub.download_url_to_file('https://models.silero.ai/models/tts/ru/v3_1_ru.pt',
                                   local_file)  

model = torch.package.PackageImporter(local_file).load_pickle("tts_models", "model")
model.to(device)

example_text = 'В недрах тундры выдры в г+етрах т+ырят в вёдра ядра кедров.'
sample_rate = 48000
speaker='baya'

audio_paths = model.save_wav(text=example_text,
                             speaker=speaker,
                             sample_rate=sample_rate)

SSML

Check out our TTS Wiki page.

Indic languages

Example

(!!!) All input sentences should be romanized to ISO format using aksharamukha tool. An example for hindi:

# V3
import torch
from aksharamukha import transliterate

# Loading model
model, example_text = torch.hub.load(repo_or_dir='snakers4/silero-models',
                                     model='silero_tts',
                                     language='indic',
                                     speaker='v3_indic')

orig_text = "प्रसिद्द कबीर अध्येता, पुरुषोत्तम अग्रवाल का यह शोध आलेख, उस रामानंद की खोज करता है"
roman_text = transliterate.process('Devanagari', 'ISO', orig_text)
print(roman_text)

audio = model.apply_tts(roman_text,
                        speaker='hindi_male')

Supported languages

Language	Speakers	Romanization function
hindi	`hindi_female`, `hindi_male`	`transliterate.process('Devanagari', 'ISO', orig_text)`
malayalam	`malayalam_female`, `malayalam_male`	`transliterate.process('Malayalam', 'ISO', orig_text)`
manipuri	`manipuri_female`	`transliterate.process('Bengali', 'ISO', orig_text)`
bengali	`bengali_female`, `bengali_male`	`transliterate.process('Bengali', 'ISO', orig_text)`
rajasthani	`rajasthani_female`, `rajasthani_female`	`transliterate.process('Devanagari', 'ISO', orig_text)`
tamil	`tamil_female`, `tamil_male`	`transliterate.process('Tamil', 'ISO', orig_text, pre_options=['TamilTranscribe'])`
telugu	`telugu_female`, `telugu_male`	`transliterate.process('Telugu', 'ISO', orig_text)`
gujarati	`gujarati_female`, `gujarati_male`	`transliterate.process('Gujarati', 'ISO', orig_text)`
kannada	`kannada_female`, `kannada_male`	`transliterate.process('Kannada', 'ISO', orig_text)`

Text-Enhancement

Languages	Quantization	Quality	Colab
'en', 'de', 'ru', 'es'	✔️	link

Dependencies

Basic dependencies for colab examples:

torch, 1.9+;
pyyaml, but it's installed with torch itself

Standalone Use

Standalone usage just requires PyTorch 1.9+ and python standard library;
Please see the detailed examples in Colab;

import torch

model, example_texts, languages, punct, apply_te = torch.hub.load(repo_or_dir='snakers4/silero-models',
                                                                  model='silero_te')

input_text = input('Enter input text\n')
apply_te(input_text, lan='en')

FAQ

Wiki

Also check out our wiki.

Performance and Quality

Please refer to this wiki sections:

Adding new Languages

Please refer here.

Contact

Get in Touch

Try our models, create an issue, join our chat, email us, read our news.

Commercial Inquiries

Please see our wiki and tiers for relevant information and email us.

Citations

@misc{Silero Models,
  author = {Silero Team},
  title = {Silero Models: pre-trained enterprise-grade STT / TTS models and benchmarks},
  year = {2021},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/snakers4/silero-models}},
  commit = {insert_some_commit_here},
  email = {hello@silero.ai}
}

Donations

Please use the "sponsor" button.

silero dependencies

omegaconf torch torchaudio

FAQs

What is silero?

Silero Models: pre-trained enterprise-grade STT / TTS models and benchmarks. Visit Snyk Advisor to see a full health score report for silero, including popularity, security, maintenance & community analysis.

Is silero popular?

The python package silero receives a total of 1,118 weekly downloads. As such, silero popularity was classified as a recognized. Visit the popularity section on Snyk Advisor to see the full health analysis.

Is silero well maintained?

We found indications that silero is an Inactive project. See the full package health analysis to learn more about the package maintenance status.

Is silero safe to use?

While scanning the latest version of silero, we found that a security review is needed. A total of 0 vulnerabilities or license issues were detected. See the full security scan results.

Last updated on 25 November-2024, at 16:46 (UTC).

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Readme

Silero Models

Installation and Basics

Speech-To-Text

Dependencies

PyTorch

ONNX

TensorFlow

Text-To-Speech

Models and Speakers

V3

Dependencies

PyTorch

Standalone Use

SSML

Indic languages

Example

Supported languages

Text-Enhancement

Dependencies

Standalone Use

FAQ

Wiki

Performance and Quality

Adding new Languages

Contact

Get in Touch

Commercial Inquiries

Citations

Further reading

English

Chinese

Russian

Donations

silero dependencies

FAQs

What is silero?

Is silero popular?

Is silero well maintained?

Is silero safe to use?

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Select a recommended open source package

Scan your app for vulnerabilities

Source Code

Open Source

Container

Config

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