
# Spoken_language_identification

## Model description

This is a spoken language recognition model trained on 2k hours of private dataset using Tensorflow. Approximately 150 hours of speech supervision per language.
the model uses the CRNN-Attention architecture that has previously been used for extracting utterance-level feature representations.
The system is trained with recordings sampled at 16kHz, single channel, and 16-bit Signed Integer PCM encoding.

More details can be found here: [**GitHub**](https://github.com/SpeechFlow-io/Spoken_language_identification)

The model can classify a speech utterance according to the language spoken. It covers 13 different languages.

| Molde Parameters  |    Supported Languages   |
|----------|--------------------------|
|   1 M       | chinese, english, french, german, indonesian, italian, japanese, korean, portuguese, russian, spanish, turkish, vietnamese|

## Example
[![ Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/16-Nre8aDvn0wN2dsgGa3xUsZ7S61e1h8#scrollTo=Is60zUMuPqSi)
Please see the provided Colab for details for runing an example.
#### How to use

```python

import librosa
from huggingface_hub import from_pretrained_keras
from featurizers.speech_featurizers import TFSpeechFeaturizer,
model = from_pretrained_keras("SpeechFlow/spoken_language_identification")
signal, _ = librosa.load(wav_path, sr=16000)
output, prob = model.predict_pb(signal)
print(output)

```
{"language": ["en", "zh", "ko", "ja", "de", "fr", "es", "pt", "vi", "tr", "it", "ru", "id"], "license": "apache-2.0", "tags": ["keras", "tensorflow"], "metrics": ["accuracy"], "libraries": "TensorBoard", "pipeline_tag": "audio-classification"}
keras tensorflow audio-classification en zh ko ja de fr es pt vi tr it ru id license:apache-2.0 region:us
TAGS
#keras #tensorflow #audio-classification #en #zh #ko #ja #de #fr #es #pt #vi #tr #it #ru #id #license-apache-2.0 #region-us 

Spoken\_language\_identification
================================


Model description
-----------------


This is a spoken language recognition model trained on 2k hours of private dataset using Tensorflow. Approximately 150 hours of speech supervision per language.
the model uses the CRNN-Attention architecture that has previously been used for extracting utterance-level feature representations.
The system is trained with recordings sampled at 16kHz, single channel, and 16-bit Signed Integer PCM encoding.


More details can be found here: GitHub


The model can classify a speech utterance according to the language spoken. It covers 13 different languages.



Example
-------


![ Open In Colab](URL
Please see the provided Colab for details for runing an example.


#### How to use



#### How to use
TAGS
#keras #tensorflow #audio-classification #en #zh #ko #ja #de #fr #es #pt #vi #tr #it #ru #id #license-apache-2.0 #region-us 
 #### How to use
TAGS
#keras #tensorflow #audio-classification #en #zh #ko #ja #de #fr #es #pt #vi #tr #it #ru #id #license-apache-2.0 #region-us 
#### How to use