forked from phoenix/litellm-mirror
303 lines
8.3 KiB
Python
303 lines
8.3 KiB
Python
# What is this?
|
|
## unit tests for openai tts endpoint
|
|
|
|
import asyncio
|
|
import os
|
|
import random
|
|
import sys
|
|
import time
|
|
import traceback
|
|
import uuid
|
|
|
|
from dotenv import load_dotenv
|
|
|
|
load_dotenv()
|
|
import os
|
|
|
|
sys.path.insert(
|
|
0, os.path.abspath("../..")
|
|
) # Adds the parent directory to the system path
|
|
from pathlib import Path
|
|
from unittest.mock import AsyncMock, MagicMock, patch
|
|
|
|
import openai
|
|
import pytest
|
|
|
|
import litellm
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"sync_mode",
|
|
[True, False],
|
|
)
|
|
@pytest.mark.parametrize(
|
|
"model, api_key, api_base",
|
|
[
|
|
(
|
|
"azure/azure-tts",
|
|
os.getenv("AZURE_SWEDEN_API_KEY"),
|
|
os.getenv("AZURE_SWEDEN_API_BASE"),
|
|
),
|
|
("openai/tts-1", os.getenv("OPENAI_API_KEY"), None),
|
|
],
|
|
) # ,
|
|
@pytest.mark.asyncio
|
|
async def test_audio_speech_litellm(sync_mode, model, api_base, api_key):
|
|
speech_file_path = Path(__file__).parent / "speech.mp3"
|
|
|
|
if sync_mode:
|
|
response = litellm.speech(
|
|
model=model,
|
|
voice="alloy",
|
|
input="the quick brown fox jumped over the lazy dogs",
|
|
api_base=api_base,
|
|
api_key=api_key,
|
|
organization=None,
|
|
project=None,
|
|
max_retries=1,
|
|
timeout=600,
|
|
client=None,
|
|
optional_params={},
|
|
)
|
|
|
|
from litellm.llms.OpenAI.openai import HttpxBinaryResponseContent
|
|
|
|
assert isinstance(response, HttpxBinaryResponseContent)
|
|
else:
|
|
response = await litellm.aspeech(
|
|
model=model,
|
|
voice="alloy",
|
|
input="the quick brown fox jumped over the lazy dogs",
|
|
api_base=api_base,
|
|
api_key=api_key,
|
|
organization=None,
|
|
project=None,
|
|
max_retries=1,
|
|
timeout=600,
|
|
client=None,
|
|
optional_params={},
|
|
)
|
|
|
|
from litellm.llms.OpenAI.openai import HttpxBinaryResponseContent
|
|
|
|
assert isinstance(response, HttpxBinaryResponseContent)
|
|
|
|
|
|
@pytest.mark.parametrize("mode", ["iterator"]) # "file",
|
|
@pytest.mark.asyncio
|
|
async def test_audio_speech_router(mode):
|
|
speech_file_path = Path(__file__).parent / "speech.mp3"
|
|
|
|
from litellm import Router
|
|
|
|
client = Router(
|
|
model_list=[
|
|
{
|
|
"model_name": "tts",
|
|
"litellm_params": {
|
|
"model": "openai/tts-1",
|
|
},
|
|
},
|
|
]
|
|
)
|
|
|
|
response = await client.aspeech(
|
|
model="tts",
|
|
voice="alloy",
|
|
input="the quick brown fox jumped over the lazy dogs",
|
|
api_base=None,
|
|
api_key=None,
|
|
organization=None,
|
|
project=None,
|
|
max_retries=1,
|
|
timeout=600,
|
|
client=None,
|
|
optional_params={},
|
|
)
|
|
|
|
from litellm.llms.OpenAI.openai import HttpxBinaryResponseContent
|
|
|
|
assert isinstance(response, HttpxBinaryResponseContent)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"sync_mode",
|
|
[False, True],
|
|
)
|
|
@pytest.mark.skip(reason="local only test - we run testing using MockRequests below")
|
|
@pytest.mark.asyncio
|
|
async def test_audio_speech_litellm_vertex(sync_mode):
|
|
litellm.set_verbose = True
|
|
speech_file_path = Path(__file__).parent / "speech_vertex.mp3"
|
|
model = "vertex_ai/test"
|
|
if sync_mode:
|
|
response = litellm.speech(
|
|
model="vertex_ai/test",
|
|
input="hello what llm guardrail do you have",
|
|
)
|
|
|
|
response.stream_to_file(speech_file_path)
|
|
|
|
else:
|
|
response = await litellm.aspeech(
|
|
model="vertex_ai/",
|
|
input="async hello what llm guardrail do you have",
|
|
)
|
|
|
|
from types import SimpleNamespace
|
|
|
|
from litellm.llms.OpenAI.openai import HttpxBinaryResponseContent
|
|
|
|
response.stream_to_file(speech_file_path)
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_speech_litellm_vertex_async():
|
|
# Mock the response
|
|
mock_response = AsyncMock()
|
|
|
|
def return_val():
|
|
return {
|
|
"audioContent": "dGVzdCByZXNwb25zZQ==",
|
|
}
|
|
|
|
mock_response.json = return_val
|
|
mock_response.status_code = 200
|
|
|
|
# Set up the mock for asynchronous calls
|
|
with patch(
|
|
"litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler.post",
|
|
new_callable=AsyncMock,
|
|
) as mock_async_post:
|
|
mock_async_post.return_value = mock_response
|
|
model = "vertex_ai/test"
|
|
|
|
response = await litellm.aspeech(
|
|
model=model,
|
|
input="async hello what llm guardrail do you have",
|
|
)
|
|
|
|
# Assert asynchronous call
|
|
mock_async_post.assert_called_once()
|
|
_, kwargs = mock_async_post.call_args
|
|
print("call args", kwargs)
|
|
|
|
assert kwargs["url"] == "https://texttospeech.googleapis.com/v1/text:synthesize"
|
|
|
|
assert "x-goog-user-project" in kwargs["headers"]
|
|
assert kwargs["headers"]["Authorization"] is not None
|
|
|
|
assert kwargs["json"] == {
|
|
"input": {"text": "async hello what llm guardrail do you have"},
|
|
"voice": {"languageCode": "en-US", "name": "en-US-Studio-O"},
|
|
"audioConfig": {"audioEncoding": "LINEAR16", "speakingRate": "1"},
|
|
}
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_speech_litellm_vertex_async_with_voice():
|
|
# Mock the response
|
|
mock_response = AsyncMock()
|
|
|
|
def return_val():
|
|
return {
|
|
"audioContent": "dGVzdCByZXNwb25zZQ==",
|
|
}
|
|
|
|
mock_response.json = return_val
|
|
mock_response.status_code = 200
|
|
|
|
# Set up the mock for asynchronous calls
|
|
with patch(
|
|
"litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler.post",
|
|
new_callable=AsyncMock,
|
|
) as mock_async_post:
|
|
mock_async_post.return_value = mock_response
|
|
model = "vertex_ai/test"
|
|
|
|
response = await litellm.aspeech(
|
|
model=model,
|
|
input="async hello what llm guardrail do you have",
|
|
voice={
|
|
"languageCode": "en-UK",
|
|
"name": "en-UK-Studio-O",
|
|
},
|
|
audioConfig={
|
|
"audioEncoding": "LINEAR22",
|
|
"speakingRate": "10",
|
|
},
|
|
)
|
|
|
|
# Assert asynchronous call
|
|
mock_async_post.assert_called_once()
|
|
_, kwargs = mock_async_post.call_args
|
|
print("call args", kwargs)
|
|
|
|
assert kwargs["url"] == "https://texttospeech.googleapis.com/v1/text:synthesize"
|
|
|
|
assert "x-goog-user-project" in kwargs["headers"]
|
|
assert kwargs["headers"]["Authorization"] is not None
|
|
|
|
assert kwargs["json"] == {
|
|
"input": {"text": "async hello what llm guardrail do you have"},
|
|
"voice": {"languageCode": "en-UK", "name": "en-UK-Studio-O"},
|
|
"audioConfig": {"audioEncoding": "LINEAR22", "speakingRate": "10"},
|
|
}
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_speech_litellm_vertex_async_with_voice_ssml():
|
|
# Mock the response
|
|
mock_response = AsyncMock()
|
|
|
|
def return_val():
|
|
return {
|
|
"audioContent": "dGVzdCByZXNwb25zZQ==",
|
|
}
|
|
|
|
mock_response.json = return_val
|
|
mock_response.status_code = 200
|
|
|
|
ssml = """
|
|
<speak>
|
|
<p>Hello, world!</p>
|
|
<p>This is a test of the <break strength="medium" /> text-to-speech API.</p>
|
|
</speak>
|
|
"""
|
|
|
|
# Set up the mock for asynchronous calls
|
|
with patch(
|
|
"litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler.post",
|
|
new_callable=AsyncMock,
|
|
) as mock_async_post:
|
|
mock_async_post.return_value = mock_response
|
|
model = "vertex_ai/test"
|
|
|
|
response = await litellm.aspeech(
|
|
input=ssml,
|
|
model=model,
|
|
voice={
|
|
"languageCode": "en-UK",
|
|
"name": "en-UK-Studio-O",
|
|
},
|
|
audioConfig={
|
|
"audioEncoding": "LINEAR22",
|
|
"speakingRate": "10",
|
|
},
|
|
)
|
|
|
|
# Assert asynchronous call
|
|
mock_async_post.assert_called_once()
|
|
_, kwargs = mock_async_post.call_args
|
|
print("call args", kwargs)
|
|
|
|
assert kwargs["url"] == "https://texttospeech.googleapis.com/v1/text:synthesize"
|
|
|
|
assert "x-goog-user-project" in kwargs["headers"]
|
|
assert kwargs["headers"]["Authorization"] is not None
|
|
|
|
assert kwargs["json"] == {
|
|
"input": {"ssml": ssml},
|
|
"voice": {"languageCode": "en-UK", "name": "en-UK-Studio-O"},
|
|
"audioConfig": {"audioEncoding": "LINEAR22", "speakingRate": "10"},
|
|
}
|