First test

dev_requirements.in

@@ -1,5 +1,6 @@
 black
 build
+exceptiongroup
 invoke
 isort
 pip-tools

dev_requirements.txt

@@ -1,5 +1,5 @@
 #
-# This file is autogenerated by pip-compile with Python 3.11
+# This file is autogenerated by pip-compile with Python 3.10
 # by the following command:
 #
 #    pip-compile dev_requirements.in
@@ -18,6 +18,10 @@ click==8.1.3
     #   pip-tools
 distlib==0.3.6
     # via virtualenv
+exceptiongroup==1.1.1
+    # via
+    #   -r dev_requirements.in
+    #   pytest
 filelock==3.12.0
     # via virtualenv
 identify==2.5.23
@@ -59,6 +63,12 @@ ruff==0.0.263
     # via -r dev_requirements.in
 shellcheck-py==0.9.0.2
     # via -r dev_requirements.in
+tomli==2.0.1
+    # via
+    #   black
+    #   build
+    #   pyproject-hooks
+    #   pytest
 virtualenv==20.23.0
     # via pre-commit
 wheel==0.40.0

local_whisper/audio.py

@@ -0,0 +1,47 @@
+from typing import BinaryIO
+
+import ffmpeg
+import numpy as np
+
+DEFAULT_SAMPLE_RATE = 16000
+
+
+# TODO probably can offload this on a worker queue too
+def load_audio(file: BinaryIO, encode=True, sr: int = DEFAULT_SAMPLE_RATE):
+    """
+    Open an audio file object and read as mono waveform, resampling as necessary.
+    Modified from https://github.com/openai/whisper/blob/main/whisper/audio.py
+    to accept a file object
+
+    Parameters
+    ----------
+    file: BinaryIO
+        The audio file like object
+    encode: Boolean
+        If true, encode audio stream to WAV before sending to whisper
+    sr: int
+        The sample rate to resample the audio if necessary
+    Returns
+    -------
+    A NumPy array containing the audio waveform, in float32 dtype.
+    """
+    if encode:
+        try:
+            # This launches a subprocess to decode audio while down-mixing and resampling as necessary.
+            # Requires the ffmpeg CLI and `ffmpeg-python` package to be installed.
+            out, _ = (
+                ffmpeg.input("pipe:", threads=0)
+                .output("-", format="s16le", acodec="pcm_s16le", ac=1, ar=sr)
+                .run(
+                    cmd="ffmpeg",
+                    capture_stdout=True,
+                    capture_stderr=True,
+                    input=file.read(),
+                )
+            )
+        except ffmpeg.Error as e:
+            raise RuntimeError(f"Failed to load audio: {e.stderr.decode()}") from e
+    else:
+        out = file.read()
+
+    return np.frombuffer(out, np.int16).flatten().astype(np.float32) / 32768.0

local_whisper/webservice.py

@@ -1,15 +1,13 @@
 import os
 from threading import Lock
-from typing import BinaryIO
 
-import ffmpeg
-import numpy as np
 import torch
 import whisper
 from fastapi import FastAPI, File, UploadFile
 from fastapi.responses import RedirectResponse
 
 from . import __version__
+from .audio import load_audio
 
 # TODO use pydantic config
 model_name = os.getenv("ASR_MODEL", "base")
@@ -43,47 +41,6 @@ def transcribe(
     return result
 
 
-# TODO probably can offload this on a worker queue too
-def load_audio(file: BinaryIO, encode=True, sr: int = SAMPLE_RATE):
-    """
-    Open an audio file object and read as mono waveform, resampling as necessary.
-    Modified from https://github.com/openai/whisper/blob/main/whisper/audio.py
-    to accept a file object
-
-    Parameters
-    ----------
-    file: BinaryIO
-        The audio file like object
-    encode: Boolean
-        If true, encode audio stream to WAV before sending to whisper
-    sr: int
-        The sample rate to resample the audio if necessary
-    Returns
-    -------
-    A NumPy array containing the audio waveform, in float32 dtype.
-    """
-    if encode:
-        try:
-            # This launches a subprocess to decode audio while down-mixing and resampling as necessary.
-            # Requires the ffmpeg CLI and `ffmpeg-python` package to be installed.
-            out, _ = (
-                ffmpeg.input("pipe:", threads=0)
-                .output("-", format="s16le", acodec="pcm_s16le", ac=1, ar=sr)
-                .run(
-                    cmd="ffmpeg",
-                    capture_stdout=True,
-                    capture_stderr=True,
-                    input=file.read(),
-                )
-            )
-        except ffmpeg.Error as e:
-            raise RuntimeError(f"Failed to load audio: {e.stderr.decode()}") from e
-    else:
-        out = file.read()
-
-    return np.frombuffer(out, np.int16).flatten().astype(np.float32) / 32768.0
-
-
 app = FastAPI(
     title="Local Whisper",
     description="Making OpenAPI's Open Whisper available via ReST API locally.",

pyinvoke/dev.py

@@ -23,3 +23,9 @@ def install_pre_commit(c):
 def build(c):
     """Builds wheel and source distributions of project."""
     pass
+
+
+@task
+def serve_dev(c):
+    """Runs the FastAPI webservice"""
+    c.run("uvicorn local_whisper.webservice:app --reload")

tests/unit/conftest.py

@@ -0,0 +1,9 @@
+import os
+
+import pytest
+
+
+@pytest.fixture
+def sample_audio():
+    audio_path = os.path.join(os.path.dirname(__file__), "sample_data/drew6.wav")
+    return audio_path

tests/unit/test_audio.py

@@ -0,0 +1,16 @@
+from local_whisper.audio import DEFAULT_SAMPLE_RATE, load_audio
+
+
+def test_audio(sample_audio):
+    print(sample_audio)
+    with open(sample_audio, mode="rb") as f:
+        audio = load_audio(f)
+    # Assert Mono
+    assert audio.ndim == 1
+    # Test the file length is appropriate size
+    assert DEFAULT_SAMPLE_RATE * 8 < audio.shape[0] < DEFAULT_SAMPLE_RATE * 12
+    # Taking the standard diviation of audio data can be used to check
+    # Amplitude Variability, Noise Detection, or Normalization. Hear we just want 
+    # to make certain it does not contain a lot of noise.
+    assert 0 < audio.std() < 1
+ 

tests/unit/test_example.py

@@ -1,2 +0,0 @@
-def test_example():
-    assert True