README.md

---
license: apache-2.0
metrics:
- accuracy
language:
- en
- zh
- ko
- ja
- de
- fr
- es
- pt
- vi
- tr
- it
- ru
- id
tags:
- keras
- tensorflow
libraries: TensorBoard
pipeline_tag: audio-classification
---

# 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)

```