Automatic Speech Recognition (ASR) is an exciting technology that allows us to transcribe spoken language into text. This blog will guide you through using ASR systems, specifically with the SpeechBrain library, to transcribe Kinyarwanda audio files effortlessly.

What You’ll Need

• A computer with Python installed.
• Basic knowledge of how to run Python scripts.
• Audio files in Kinyarwanda for transcription.

Step 1: Install SpeechBrain

The first step in your ASR journey involves installing the SpeechBrain library along with necessary dependencies. Simply open your terminal or command prompt and enter the following command:

pip install speechbrain transformers

Once the installation is complete, you’re ready to advance to the next steps.

Step 2: Transcribing Your Own Audio Files

Now that you have SpeechBrain installed, let’s transcribe some audio. This process is akin to having a personal assistant who listens to a recorded meeting and types out what everyone said. Follow these steps:

from speechbrain.pretrained import EncoderDecoderASR

asr_model = EncoderDecoderASR.from_hparams(source="speechbrain/asr-wav2vec2-commonvoice-rw", savedir="pretrained_models/asr-wav2vec2-commonvoice-rw")
asr_model.transcribe_file("speechbrain/asr-wav2vec2-commonvoice-rw/example.mp3")

This code sets up the ASR model and transcribes the example audio file. Replace “example.mp3” with the path to your Kinyarwanda audio file.

Step 3: Transcribing on GPU

If you have a GPU and want to speed things up, you can enable GPU usage with a simple option. Modify your code snippet as follows:

asr_model = EncoderDecoderASR.from_hparams(source="speechbrain/asr-wav2vec2-commonvoice-rw", savedir="pretrained_models/asr-wav2vec2-commonvoice-rw", run_opts={"device": "cuda"})

Step 4: Parallel Inference on a Batch

For those looking to transcribe multiple audio files simultaneously—a bit like a busy office where many calls come in at once—this is your solution. You can check out this Colab notebook that shows how to accomplish batch transcriptions.

Step 5: Training Your Own Model

Interested in training your model? You can train from scratch using SpeechBrain. This is similar to teaching someone new how to do a task:

1. Clone the SpeechBrain repository:

git clone https://github.com/speechbrain/speechbrain

2. Navigate to the SpeechBrain folder:

cd speechbrain

3. Install the necessary requirements:

pip install -r requirements.txt
pip install -e .

4. Finally, run the training script:

cd recipes/CommonVoiceASR/seq2seq
python train_with_wav2vec.py hparams/train_rw_with_wav2vec.yaml --data_folder=your_data_folder

After training, you can find your results, logs, and models here.

Troubleshooting

If you encounter issues while following these steps, here are some troubleshooting ideas:

• Ensure that Python and pip are installed and up to date.
• Double-check the paths to your audio files.
• If using GPU, verify that your environment supports CUDA.
• Refer to the SpeechBrain documentation for additional support.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Limitations

Note that the SpeechBrain team does not provide any warranty on the performance of this ASR model outside of the datasets it was trained on. Always validate the output based on your specific needs.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.