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;

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.

Currently we provide the following checkpoints:

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

Model flavours:

	jit	jit	jit	jit_q	jit_q	onnx	onnx	onnx
	xsmall	small	large	xsmall	small	xsmall	small	large
English (`en_v4_0`)			✔️					✔️
English (`en_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.

Currently we provide the following speakers:

Speaker	Auto-stress	Language	SR
`aidar_v2`	yes	`ru`	`8000`, `16000`
`baya_v2`	yes	`ru`	`8000`, `16000`
`irina_v2`	yes	`ru`	`8000`, `16000`
`kseniya_v2`	yes	`ru`	`8000`, `16000`
`natasha_v2`	yes	`ru`	`8000`, `16000`
`ruslan_v2`	yes	`ru`	`8000`, `16000`
`lj_v2`	no	`en`	`8000`, `16000`
`thorsten_v2`	no	`de`	`8000`, `16000`
`tux_v2`	no	`es`	`8000`, `16000`
`gilles_v2`	no	`fr`	`8000`, `16000`
`multi_v2`	no	`ru`, `en`, `de`, `es`, `fr`, `tt`	`8000`, `16000`

(!!!) In multi_v2 all speakers can speak all of langauges (with various levels of fidelity).

Dependencies

Basic dependencies for colab examples:

torch, 1.9+;
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

import torch

language = 'ru'
speaker = 'kseniya_v2'
sample_rate = 16000
device = torch.device('cpu')

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

audio = model.apply_tts(texts=[example_text],
                        sample_rate=sample_rate)

Standalone Use

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

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/v2_kseniya.pt',
                                   local_file)  

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

example_batch = ['В недрах тундры выдры в г+етрах т+ырят в вёдра ядра кедров.',
                 'Котики - это жидкость!',
                 'М+ама М+илу м+ыла с м+ылом.']
sample_rate = 16000

audio_paths = model.save_wav(texts=example_batch,
                             sample_rate=sample_rate)

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}
}

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kandy/silero-models

Silero Models

Speech-To-Text

Dependencies

PyTorch

ONNX

TensorFlow

Text-To-Speech

Models and Speakers

Dependencies

PyTorch

Standalone Use

FAQ

Wiki

Performance and Quality

Adding new Languages

Contact

Get in Touch

Commercial Inquiries

Citations

Further reading

English

Chinese

Russian

Donations