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# coding=utf-8
# --------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for
# license information.
# --------------------------------------------------------------------------
"""
FILE: sample_transcribe_with_phrase_list.py
DESCRIPTION:
This sample demonstrates how to transcribe an audio file with a custom phrase list
to improve recognition accuracy for domain-specific terminology using the Azure AI Transcription client.
USAGE:
python sample_transcribe_with_phrase_list.py
Set the environment variables with your own values before running the sample:
1) AZURE_SPEECH_ENDPOINT - the endpoint to your Speech resource.
2) AZURE_SPEECH_API_KEY - your Speech API key.
"""
import os
def sample_transcribe_with_phrase_list():
# [START transcribe_with_phrase_list]
from azure.core.credentials import AzureKeyCredential
from azure.ai.transcription import TranscriptionClient
from azure.ai.transcription.models import (
TranscriptionContent,
TranscriptionOptions,
PhraseListProperties,
)
# Get configuration from environment variables
endpoint = os.environ["AZURE_SPEECH_ENDPOINT"]
api_key = os.environ["AZURE_SPEECH_API_KEY"]
# Create the transcription client
client = TranscriptionClient(endpoint=endpoint, credential=AzureKeyCredential(api_key))
# Path to your audio file with domain-specific terminology
import pathlib
audio_file_path = pathlib.Path(__file__).parent / "assets" / "audio.wav"
# Open and read the audio file
with open(audio_file_path, "rb") as audio_file:
# Create a phrase list with custom terminology
# This helps improve recognition accuracy for specific words
phrase_list = PhraseListProperties(
phrases=["Azure", "Cognitive Services", "Speech SDK", "TranscriptionClient", "Kubernetes", "microservices"],
biasing_weight=5.0, # Weight between 1.0 and 20.0 (higher = more bias)
)
# Create transcription options with phrase list
options = TranscriptionOptions(locales=["en-US"], phrase_list=phrase_list)
# Create the request content
request_content = TranscriptionContent(definition=options, audio=audio_file)
# Transcribe the audio
result = client.transcribe(request_content)
# Print the transcription result
print("Transcription with custom phrase list:")
print(f"{result.combined_phrases[0].text}")
# Print individual phrases if available
if result.phrases:
print("\nDetailed phrases:")
for phrase in result.phrases:
print(f" [{phrase.offset_milliseconds}ms]: {phrase.text}")
# [END transcribe_with_phrase_list]
if __name__ == "__main__":
sample_transcribe_with_phrase_list()
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