In this guide, we will explore how to set up a streaming API for Automatic Speech Recognition (ASR) using the Conformer-Transducer model provided by SpeechBrain. This implementation will enable your applications to process audio in real-time and convert speech to text dynamically. Let’s dive in!

Prerequisites

• Python installed on your machine
• Basic understanding of Python programming
• Familiarity with command line interface
• Access to your audio files or streams

Step 1: Install SpeechBrain

The first step to using the Conformer model is to install the SpeechBrain library. You can do this with the following command:

pip install speechbrain

Step 2: Prepare Your Audio Files

You can use audio files sampled at 16kHz. If your audio isn’t in this format, don’t worry! The SpeechBrain library will automatically normalize your audio when transcribing.

Step 3: Transcribing Audio Files

To transcribe your audio, you will execute a Python script. Here’s how you might set it up:

from speechbrain.inference.ASR import StreamingASR
from speechbrain.utils.dynamic_chunk_training import DynChunkTrainConfig

asr_model = StreamingASR.from_hparams(
    source="speechbrain/asr-streaming-conformer-librispeech",
    savedir="pretrained_models/asr-streaming-conformer-librispeech"
)

asr_model.transcribe_file(
    "test-en.wav",
    DynChunkTrainConfig(24, 4),
    use_torchaudio_streaming=False,
)

Understanding the Code

To understand the transcription code better, let’s use an analogy. Think of your audio file as a big puzzle of sound. The StreamingASR model acts as a skilled craftsman who carefully picks up pieces of the puzzle (chunks of audio), examines them, and joins them together to form the complete picture (text). Here’s how it works:

• The model looks for pieces that can fit together by selecting a chunk size of ~960ms.
• It analyzes each piece with a little context from previous ones (in our example, 4 chunks before the current one).
• The result is that you receive a text interpretation of the audio almost in real-time!

Step 4: Live Streaming with ffmpeg

If you want to transcribe live audio, for instance, from a radio stream, you can utilize the following command:

python3 asr.py http://as-hls-ww-live.akamaized.net/pool_904/live/wwbbc_radio_fourfm/bbc_radio_fourfm.isml/bbc_radio_fourfm-audio%3d96000.norewind.m3u8 --model-source=speechbrain/asr-streaming-conformer-librispeech --device=cpu -v

Troubleshooting Common Issues

While implementing the streaming API, you might encounter some issues. Here are some common problems and their solutions:

• Problem: Audio files fail to transcribe.
  Solution: Ensure your audio is in the correct format (16kHz, mono channel). The library will normalize it, but it’s best to start with the right format.
• Problem: Performance issues during streaming.
  Solution: Try reducing the chunk size or limiting left context chunks to reduce latency.
• Problem: Errors in loading the model.
  Solution: Double-check the model source path and ensure it’s correctly downloaded to your designated directory.

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Conclusion

By following these steps, you should now be able to implement a streaming API for Automatic Speech Recognition using the SpeechBrain library effectively. This opens up countless possibilities for integrating ASR into your applications, enhancing usability, and creating interactive experiences.

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.