whisper-tools 0.1.2

Real-time speech recognition with Whisper

Whisper-tools

High-level python library for stream and static transcription with whisper

Getting started

Installation

pip install whisper-tools

Usage

• For transcription using a local Whisper model (downloaded automatically), create a transcriber - an object of the WhisperLocal class:

from whisper_tools import WhisperLocal

transcriber = WhisperLocal()

# WhisperLocal(model_name="openai/whisper-tiny", language='en', device='auto'))

• For transcription using an LLM via API, create a transcriber - an object of the WhisperAPI class:

from whisper_tools import WhisperAPI

transcriber_api = WhisperAPI(api_key="your_key", base_url="your_url")

# WhisperAPI(api_key="your_key", base_url="your_url", model="your_model")

Transcribing an audio file

• For local transcription: text = transcriber.transcribe_file('path-to-file.wav')
• For transcription using an LLM via API: text = transcriber.transcribe_file_api('path-to-file.wav')

Real-time transcription

[!IMPORTANT]
True streaming transcription requires modifications to the Whisper architecture, as the original model expects a complete audio file, so we send information in chunks.

• For local transcription: create an object of the StreamRecorder class and pass it our transcriber recorder = StreamRecorder(WhisperLocal())
• For transcription using an LLM via API: create an object of the StreamRecorderAPI class and pass it our transcriber recorder = StreamRecorder(WhisperAPI(api_key="your_key", base_url="your_url"))

try:
    # start recording
    recorder.start_recording()
    print("Recording... Press Ctrl+C to stop")
    while True:
        # get a chunk (block of transcribed speech)
        text = recorder.process_chunk()
        if text:
            print(f"Recognized: {text}")
except KeyboardInterrupt:
    print("\nStopping...")
finally:
    # stop recording
    recorder.stop_recording()

Examples

Transcribing an audio file locally:

from whisper_tools import WhisperLocal

text = WhisperLocal().transcribe_file("/voice example/example.wav")

print(text)

Transcribing an audio file via API::

from whisper_tools import WhisperAPI

whisper_api = WhisperAPI(api_key="your_key", base_url="your_url")
text = whisper_api.transcribe_file_api("/voice example/example.wav")

print(text)

Streaming transcription locally:

from whisper_tools import WhisperLocal, StreamRecorder

recorder = StreamRecorder(WhisperLocal())
# StreamRecorder(WhisperLocal(), sample_rate=16000, chunk_duration=5, min_interval=2.0)

try:
    recorder.start_recording()
    print("Recording... Press Ctrl+C to stop")
    while True:
        text = recorder.process_chunk()
        if text:
            print(f"Recognized: {text}")
except KeyboardInterrupt:
    print("\nStopping...")
finally:
    recorder.stop_recording()

Streaming transcription via API:

from whisper_tools import WhisperAPI, StreamRecorderAPI

recorder = StreamRecorderAPI(WhisperAPI(api_key="your_key", base_url="your_url"))
# StreamRecorderAPI(WhisperAPI(), sample_rate=16000, chunk_duration=5, min_interval=2.0)

try:
    recorder.start_recording()
    print("Recording... Press Ctrl+C to stop")
    while True:
        text = recorder.process_chunk()
        if text:
            print(f"Recognized: {text}")
except KeyboardInterrupt:
    print("\nStopping...")
finally:
    recorder.stop_recording()