---
license: apache-2.0
datasets:
- mozilla-foundation/common_voice_11_0
language:
- en
- bn
metrics:
- wer
library_name: transformers
pipeline_tag: automatic-speech-recognition
---
## Results
- WER 46

# Use with [BanglaSpeech2text](https://github.com/shhossain/BanglaSpeech2Text)

## Test it in Google Colab
- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/shhossain/BanglaSpeech2Text/blob/main/banglaspeech2text_in_colab.ipynb)

## Installation
You can install the library using pip:

```bash
pip install banglaspeech2text
```

## Usage
### Model Initialization
To use the library, you need to initialize the Speech2Text class with the desired model. By default, it uses the "base" model, but you can choose from different pre-trained models: "tiny", "small", "medium", "base", or "large". Here's an example:

```python
from banglaspeech2text import Speech2Text

stt = Speech2Text(model="base")

# You can use it wihout specifying model name (default model is "base")
stt = Speech2Text()
```

### Transcribing Audio Files
You can transcribe an audio file by calling the transcribe method and passing the path to the audio file. It will return the transcribed text as a string. Here's an example:

```python
transcription = stt.transcribe("audio.wav")
print(transcription)
```

### Use with SpeechRecognition
You can use [SpeechRecognition](https://pypi.org/project/SpeechRecognition/) package to get audio from microphone and transcribe it. Here's an example:
```python
import speech_recognition as sr
from banglaspeech2text import Speech2Text

stt = Speech2Text(model="base")

r = sr.Recognizer()
with sr.Microphone() as source:
    print("Say something!")
    audio = r.listen(source)
    output = stt.recognize(audio)

print(output)
```

### Use GPU
You can use GPU for faster inference. Here's an example:
```python

stt = Speech2Text(model="base",use_gpu=True)

```
### Advanced GPU Usage
For more advanced GPU usage you can use `device` or `device_map` parameter. Here's an example:
```python
stt = Speech2Text(model="base",device="cuda:0")
```
```python
stt = Speech2Text(model="base",device_map="auto")
```
__NOTE__: Read more about [Pytorch Device](https://pytorch.org/docs/stable/tensor_attributes.html#torch.torch.device)

### Instantly Check with gradio
You can instantly check the model with gradio. Here's an example:
```python
from banglaspeech2text import Speech2Text, available_models
import gradio as gr

stt = Speech2Text(model="base",use_gpu=True)

# You can also open the url and check it in mobile
gr.Interface(
    fn=stt.transcribe, 
    inputs=gr.Audio(source="microphone", type="filepath"), 
    outputs="text").launch(share=True)
```

__Note__: For more usecases and models -> [BanglaSpeech2Text](https://github.com/shhossain/BanglaSpeech2Text)

# Use with transformers
### Installation
```
pip install transformers
pip install torch
```

## Usage

### Use with file
```python
from transformers import pipeline

pipe = pipeline('automatic-speech-recognition','shhossain/whisper-base-bn')

def transcribe(audio_path):
  return pipe(audio_path)['text']

audio_file = "test.wav"

print(transcribe(audio_file))
```