SpeechFlow/spoken_language_identification

Spokenlanguageidentification

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

 Please see the provided Colab for details for runing an example.

How to use

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)