Files
LocalAI/backend/python/whisperx/test.py
eureka928 c8245d069d feat(whisperx): add whisperx backend for transcription with diarization
Add Python gRPC backend using WhisperX for speech-to-text with
word-level timestamps, forced alignment, and speaker diarization
via pyannote-audio when HF_TOKEN is provided.

Signed-off-by: eureka928 <meobius123@gmail.com>
2026-02-02 11:36:32 +01:00

125 lines
4.4 KiB
Python

"""
A test script to test the gRPC service for WhisperX transcription
"""
import unittest
import subprocess
import time
import os
import tempfile
import shutil
import backend_pb2
import backend_pb2_grpc
import grpc
class TestBackendServicer(unittest.TestCase):
"""
TestBackendServicer is the class that tests the gRPC service
"""
def setUp(self):
"""
This method sets up the gRPC service by starting the server
"""
self.service = subprocess.Popen(["python3", "backend.py", "--addr", "localhost:50051"])
time.sleep(10)
def tearDown(self) -> None:
"""
This method tears down the gRPC service by terminating the server
"""
self.service.terminate()
self.service.wait()
def test_server_startup(self):
"""
This method tests if the server starts up successfully
"""
try:
self.setUp()
with grpc.insecure_channel("localhost:50051") as channel:
stub = backend_pb2_grpc.BackendStub(channel)
response = stub.Health(backend_pb2.HealthMessage())
self.assertEqual(response.message, b'OK')
except Exception as err:
print(err)
self.fail("Server failed to start")
finally:
self.tearDown()
def test_load_model(self):
"""
This method tests if the model is loaded successfully
"""
try:
self.setUp()
with grpc.insecure_channel("localhost:50051") as channel:
stub = backend_pb2_grpc.BackendStub(channel)
response = stub.LoadModel(backend_pb2.ModelOptions(Model="tiny"))
self.assertTrue(response.success)
self.assertEqual(response.message, "Model loaded successfully")
except Exception as err:
print(err)
self.fail("LoadModel service failed")
finally:
self.tearDown()
def test_audio_transcription(self):
"""
This method tests if audio transcription works successfully
"""
# Create a temporary directory for the audio file
temp_dir = tempfile.mkdtemp()
audio_file = os.path.join(temp_dir, 'audio.wav')
try:
# Download the audio file to the temporary directory
print(f"Downloading audio file to {audio_file}...")
url = "https://cdn.openai.com/whisper/draft-20220913a/micro-machines.wav"
result = subprocess.run(
["wget", "-q", url, "-O", audio_file],
capture_output=True,
text=True
)
if result.returncode != 0:
self.fail(f"Failed to download audio file: {result.stderr}")
# Verify the file was downloaded
if not os.path.exists(audio_file):
self.fail(f"Audio file was not downloaded to {audio_file}")
self.setUp()
with grpc.insecure_channel("localhost:50051") as channel:
stub = backend_pb2_grpc.BackendStub(channel)
# Load the model first
load_response = stub.LoadModel(backend_pb2.ModelOptions(Model="tiny"))
self.assertTrue(load_response.success)
# Perform transcription without diarization
transcript_request = backend_pb2.TranscriptRequest(dst=audio_file)
transcript_response = stub.AudioTranscription(transcript_request)
# Print the transcribed text for debugging
print(f"Transcribed text: {transcript_response.text}")
print(f"Number of segments: {len(transcript_response.segments)}")
# Verify response structure
self.assertIsNotNone(transcript_response)
self.assertIsNotNone(transcript_response.text)
self.assertGreater(len(transcript_response.text), 0)
self.assertGreater(len(transcript_response.segments), 0)
# Verify segments have timing info
segment = transcript_response.segments[0]
self.assertIsNotNone(segment.text)
self.assertIsInstance(segment.id, int)
except Exception as err:
print(err)
self.fail("AudioTranscription service failed")
finally:
self.tearDown()
# Clean up the temporary directory
if os.path.exists(temp_dir):
shutil.rmtree(temp_dir)