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https://github.com/shhossain/banglaspeech2text

BanglaSpeech2Text: An open-source offline speech-to-text package for Bangla language. Fine-tuned on the latest whisper speech to text model for optimal performance.
https://github.com/shhossain/banglaspeech2text

bangla bangla-asr bangla-automatic-speech-recognition bangla-speech-recognition bangla-speech-to-text bangla-voice-recognition deep-learning hacktoberfest machine-learning pytorch speech speech-recognition speech-to-text transformer voice-recognition whisper whisper-model

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BanglaSpeech2Text: An open-source offline speech-to-text package for Bangla language. Fine-tuned on the latest whisper speech to text model for optimal performance.

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README 

        
# BanglaSpeech2Text (Bangla Speech to Text)



BanglaSpeech2Text: An open-source offline speech-to-text package for Bangla language. Fine-tuned on the latest whisper speech to text model for optimal performance. Transcribe speech to text, convert voice to text and perform speech recognition in python with ease, even without internet connection.



## [Models](https://github.com/shhossain/BanglaSpeech2Text/blob/main/banglaspeech2text/utils/listed_models.json)



| Model   | Size       | Best(WER) |

| ------- | ---------- | --------- |

| `tiny`  |100-200 MB | 74        |

| `base`  |200-300 MB | 46        |

| `small` |1 GB       | 18        |

| `large` |3-4 GB     | 11        |



**NOTE**: Bigger model have better accuracy but slower inference speed. More models [HuggingFace Model Hub](https://huggingface.co/models?pipeline_tag=automatic-speech-recognition&language=bn&sort=likes)



## Pre-requisites



- Python 3.7 or higher



## 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 `recognize` 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.recognize("audio.wav")

print(transcription)

```



### For longer audio files

(As different models have different max audio length, so you can use the following methods to transcribe longer audio files)



For longer audio files, you can use the `generate` or `recognize` method. Here's an example:



```python

for text in stt.generate("audio.wav"): # it will generate text as the chunks are processed

    print(text)



# or

texts = stt.recognize("audio.wav", split=True) # Probably faster than generate method

for text in texts:

    print(text)



# or



# you can pass min_silence_length and silence_threshold to split_on_silence

text = stt.recognize("audio.wav", split=True, min_silence_length=1000, silence_threshold=-16)

print(text)

```



## Multiple Audio Formats



BanglaSpeech2Text supports the following audio formats for input:



- File Formats: WAV, MP3, FLAC, and all formats supported by FFmpeg.

- Bytes: Raw audio data in byte format.

- Numpy Array: Numpy array representing audio data, preferably obtained using librosa.load.

- AudioData: Audio data obtained from the speech_recognition library.

- AudioSegment: Audio segment objects from the pydub library.

- BytesIO: Audio data provided through BytesIO objects from the io module.

- Path: Pathlib Path object pointing to an audio file.



Here's an example:



```python

transcription = stt.recognize("audio.mp3")

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!")

    r.adjust_for_ambient_noise(source)

    audio = r.listen(source)

    output = stt.recognize(audio)



print(output)

```



### Use CPU



You can use GPU for faster inference. Here's an example:



```python



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



```



### 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.recognize,

    inputs=gr.Audio(source="microphone", type="filepath"),

    outputs="text").launch(share=True)

```



## Some more usage examples



### Use huggingface model



```python

stt = Speech2Text(model="openai/whisper-tiny")

```



### Change Model Save location



```python

import os

os.environ["BANGLASPEECH2TEXT_CACHE"] = "/path/to/cache"



stt = Speech2Text(model="base")

```



### See current model info



```python

stt = Speech2Text(model="base")



print(stt.model_name) # the name of the model

print(stt.model_size) # the size of the model

print(stt.model_license) # the license of the model

print(stt.model_description) # the description of the model(in .md format)

print(stt.model_url) # the url of the model

print(stt.model_wer) # word error rate of the model

```



### CLI



You can use the library from the command line. Here's an example:



```bash

bnstt 'file.wav'

```



You can also use it with microphone:



```bash

bnstt --mic

```



Other options:



```bash

usage: bnstt

       [-h]

       [-gpu]

       [-c CACHE]

       [-o OUTPUT]

       [-m MODEL]

       [-s]

       [-sm MIN_SILENCE_LENGTH]

       [-st SILENCE_THRESH]

       [-sp PADDING]

       [--list]

       [--info]

       [INPUT ...]



Bangla Speech to Text



positional arguments:

  INPUT

    inputfile(s) or list of files



options:

  -h, --help

    show this help message and exit

  -gpu

    use gpu

  -c CACHE, --cache CACHE

    cache directory

  -o OUTPUT, --output OUTPUT

    output directory

  -m MODEL, --model MODEL

    model name

  -s, --split

    split audio file using pydub split_on_silence

  -sm MIN_SILENCE_LENGTH, --min_silence_length MIN_SILENCE_LENGTH Minimum length of silence to split on (in ms)

  -st SILENCE_THRESH, --silence_thresh SILENCE_THRESH dBFS below reference to be considered silence

  -sp PADDING, --padding PADDING Padding to add to beginning and end of each split (in ms)

  --list list of available models

  --info show model info

```



## Custom Use Cases and Support



If your business or project has specific speech-to-text requirements that go beyond the capabilities of the provided open-source package, I'm here to help! I understand that each use case is unique, and I'm open to collaborating on custom solutions that meet your needs. Whether you have longer audio files that need accurate transcription, require model fine-tuning, or need assistance in implementing the package effectively, I'm available for support.