Description
Pretrained Wav2vec2 model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.asr_bp_tedx100_xlsr is a Portuguese model originally trained by lgris.
NOTE: This model only works on a GPU, if you need to use this model on a CPU device please use asr_bp_tedx100_xlsr
How to use
audio_assembler = AudioAssembler() \
.setInputCol("audio_content") \
.setOutputCol("audio_assembler")
speech_to_text = Wav2Vec2ForCTC \
.pretrained("asr_bp_tedx100_xlsr_gpu", "pt")\
.setInputCols("audio_assembler") \
.setOutputCol("text")
pipeline = Pipeline(stages=[
audio_assembler,
speech_to_text,
])
pipelineModel = pipeline.fit(audioDf)
pipelineDF = pipelineModel.transform(audioDf)
val audioAssembler = new AudioAssembler()
.setInputCol("audio_content")
.setOutputCol("audio_assembler")
val speechToText = Wav2Vec2ForCTC
.pretrained("asr_bp_tedx100_xlsr_gpu", "pt")
.setInputCols("audio_assembler")
.setOutputCol("text")
val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText))
val pipelineModel = pipeline.fit(audioDf)
val pipelineDF = pipelineModel.transform(audioDf)
import nlu
import requests
response = requests.get('https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/resources/en/audio/samples/wavs/ngm_12484_01067234848.wav')
with open('ngm_12484_01067234848.wav', 'wb') as f:
f.write(response.content)
nlu.load("pt.speech2text.wav2vec_xlsr.tedx100.gpu.by_lgris").predict("ngm_12484_01067234848.wav")
Model Information
| Model Name: | asr_bp_tedx100_xlsr_gpu |
| Compatibility: | Spark NLP 4.2.0+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [audio_assembler] |
| Output Labels: | [text] |
| Language: | pt |
| Size: | 756.1 MB |