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---
license: apache-2.0
base_model: facebook/wav2vec2-large-xlsr-53
tags:
- generated_from_trainer
datasets:
- common_voice_13_0
metrics:
- wer
model-index:
- name: wav2vec2-large-xlsr-mvc-swahili
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_13_0
type: common_voice_13_0
config: sw
split: test
args: sw
metrics:
- name: Wer
type: wer
value: 0.2
language:
- sw
---
# wav2vec2-large-xlsr-mvc-swahili
This model is a finetuned version of facebook/wav2vec2-large-xlsr-53.
<!--Following inspiration from [alamsher/wav2vec2-large-xlsr-53-common-voice-s](https://huggingface.co/alamsher/wav2vec2-large-xlsr-53-common-voice-sw)-->
# How to use the model
There was an issue with vocab, seems like there are special characters included and they were not considered during training
You could try
```python
from transformers import AutoProcessor, AutoModelForCTC
repo_name = "eddiegulay/wav2vec2-large-xlsr-mvc-swahili"
processor = AutoProcessor.from_pretrained(repo_name)
model = AutoModelForCTC.from_pretrained(repo_name)
# if you have GPU
# move model to CUDA
model = model.to("cuda")
def transcribe(audio_path):
# Load the audio file
audio_input, sample_rate = torchaudio.load(audio_path)
target_sample_rate = 16000
audio_input = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=target_sample_rate)(audio_input)
# Preprocess the audio data
input_dict = processor(audio_input[0], return_tensors="pt", padding=True, sampling_rate=16000)
# Perform inference and transcribe
logits = model(input_dict.input_values.to("cuda")).logits
pred_ids = torch.argmax(logits, dim=-1)[0]
transcription = processor.decode(pred_ids)
return transcription
transcript = transcribe('your_audio.mp3')
``` |