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#10 111.9 Building wheels for collected packages: efficientnet-pytorch, pretrainedmodels
#10 111.9   Building wheel for efficientnet-pytorch (pyproject.toml): started
#10 112.8   Building wheel for efficientnet-pytorch (pyproject.toml): finished with status 'done'
#10 112.8   Created wheel for efficientnet-pytorch: filename=efficientnet_pytorch-0.7.1-py3-none-any.whl size=16478 sha256=b7a876e155e2e80b64bf8d78a5018f03b5177fce0f7a4db68ad4dc8302e6822b
#10 112.8   Stored in directory: /tmp/pip-ephem-wheel-cache-_bhphf0b/wheels/8b/6f/9b/231a832f811ab6ebb1b32455b177ffc6b8b1cd8de19de70c09
#10 112.8   Building wheel for pretrainedmodels (pyproject.toml): started
#10 113.9   Building wheel for pretrainedmodels (pyproject.toml): finished with status 'done'
#10 113.9   Created wheel for pretrainedmodels: filename=pretrainedmodels-0.7.4-py3-none-any.whl size=60992 sha256=bb681385067249818f50796e803b5214eeec18aad66ea3af572b5414fafc10a7
#10 113.9   Stored in directory: /tmp/pip-ephem-wheel-cache-_bhphf0b/wheels/5f/5b/96/fd94bc35962d7c6b699e8814db545155ac91d2b95785e1b035
#10 113.9 Successfully built efficientnet-pytorch pretrainedmodels
#10 114.3 Installing collected packages: rfc3986, nvidia-cusparselt-cu12, mpmath, language-tags, flatbuffers, websockets, uvloop, urllib3, uritemplate, typing-extensions, triton, tqdm, threadpoolctl, termcolor, tenacity, sympy, sniffio, smart-open, six, shellingham, rpds-py, regex, pyyaml, python-dotenv, pyparsing, pygments, pycparser, protobuf, platformdirs, pillow, packaging, nvidia-nvtx-cu12, nvidia-nvshmem-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufile-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, numpy, networkx, munch, msgpack, mdurl, MarkupSafe, loguru, llvmlite, joblib, jmespath, jiter, isodate, idna, httptools, hf-xet, h11, fsspec, filelock, espeakng-loader, dlinfo, distro, decorator, cuda-pathfinder, colorlog, click, charset_normalizer, certifi, babel, audioread, attrs, annotated-types, annotated-doc, uvicorn, typing-inspection, soxr, scipy, requests, referencing, rdflib, python-dateutil, pydantic-core, onnxruntime, nvidia-cusparse-cu12, nvidia-cufft-cu12, nvidia-cudnn-cu12, numba, markdown-it-py, lazy_loader, jinja2, httpcore, cuda-bindings, cffi, anyio, watchfiles, starlette, soundfile, scikit-learn, rich, pydantic, pooch, nvidia-cusolver-cu12, jsonschema-specifications, httpx, botocore, typer, torch, s3transfer, openai, librosa, jsonschema, fastapi, torchvision, huggingface-hub, efficientnet-pytorch, csvw, boto3, timm, segments, pretrainedmodels, segmentation-models-pytorch, phonemizer-fork, moonshine, kokoro-onnx

Process still running.

cd /home/crogers2287/Projects/uvin

time="2026-03-01T03:53:40Z" level=warning msg="/home/crogers2287/Projects/uvin/docker-compose.yml: the attribute `version` is obsolete, it will be ignored, please remove it to avoid potential confusion"
#1 [internal] load local bake definitions
#1 reading from stdin 377B done
#1 DONE 0.0s

#2 [internal] load build definition from Dockerfile
#2 transferring dockerfile: 992B done
#2 DONE 0.0s

#3 [internal] load metadata for docker.io/library/python:3.11-slim-bookworm
#3 DONE 0.3s

#4 [internal] load .dockerignore
#4 transferring context: 2B done
#4 DONE 0.0s

#5 [1/9] FROM docker.io/library/python:3.11-slim-bookworm@sha256:65a93d69fa75478d554f4ad27c85c1e69fa184956261b4301ebaf6dbb0a3543d
#5 DONE 0.0s

#6 [internal] load build context
#6 transferring context: 7.86kB done
#6 DONE 0.0s

#7 [2/9] RUN apt-get update && apt-get install -y --no-install-recommends     build-essential curl libsndfile1 ffmpeg libopenblas-dev libgomp1     && rm -rf /var/lib/apt/lists/*
#7 CACHED

#8 [3/9] WORKDIR /app
#8 CACHED

#9 [4/9] COPY requirements.txt .
#9 DONE 0.1s

#10 [5/9] RUN pip install --upgrade pip && pip install --no-cache-dir -r requirements.txt
#10 2.157 Requirement already satisfied: pip in /usr/local/lib/python3.11/site-packages (24.0)
#10 2.427 Collecting pip
#10 2.648   Downloading pip-26.0.1-py3-none-any.whl.metadata (4.7 kB)
#10 2.677 Downloading pip-26.0.1-py3-none-any.whl (1.8 MB)
#10 2.784    ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1.8/1.8 MB 18.5 MB/s eta 0:00:00
#10 2.877 Installing collected packages: pip
#10 2.877   Attempting uninstall: pip
#10 2.880     Found existing installation: pip 24.0
#10 2.935     Uninstalling pip-24.0:
#10 3.228       Successfully uninstalled pip-24.0
#10 4.448 Successfully installed pip-26.0.1
#10 4.448 WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv
#10 6.304 Collecting fastapi<1.0.0,>=0.109.0 (from -r requirements.txt (line 4))
#10 6.568   Downloading fastapi-0.134.0-py3-none-any.whl.metadata (30 kB)
#10 6.678 Collecting uvicorn<1.0.0,>=0.27.0 (from uvicorn[standard]<1.0.0,>=0.27.0->-r requirements.txt (line 5))
#10 6.699   Downloading uvicorn-0.41.0-py3-none-any.whl.metadata (6.7 kB)
#10 6.765 Collecting python-dotenv<2.0.0,>=1.0.0 (from -r requirements.txt (line 6))
#10 6.787   Downloading python_dotenv-1.2.1-py3-none-any.whl.metadata (25 kB)
#10 6.851 Collecting httpx<1.0.0,>=0.26.0 (from -r requirements.txt (line 7))
#10 6.875   Downloading httpx-0.28.1-py3-none-any.whl.metadata (7.1 kB)
#10 6.912 Collecting moonshine>=0.1.7 (from -r requirements.txt (line 10))
#10 6.965   Downloading moonshine-0.1.7-py3-none-any.whl.metadata (5.1 kB)
#10 7.046 Collecting kokoro-onnx>=0.3.0 (from -r requirements.txt (line 13))
#10 7.074   Downloading kokoro_onnx-0.5.0-py3-none-any.whl.metadata (3.5 kB)
#10 7.215 Collecting openai<2.0.0,>=1.12.0 (from -r requirements.txt (line 16))
#10 7.239   Downloading openai-1.109.1-py3-none-any.whl.metadata (29 kB)
#10 7.518 Collecting numpy<2.0.0,>=1.24.0 (from -r requirements.txt (line 19))
#10 7.538   Downloading numpy-1.26.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (61 kB)
#10 7.733 Collecting scipy<2.0.0,>=1.11.0 (from -r requirements.txt (line 20))
#10 7.753   Downloading scipy-1.17.1-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.metadata (62 kB)
#10 7.803 Collecting soundfile<1.0.0,>=0.12.0 (from -r requirements.txt (line 21))
#10 7.823   Downloading soundfile-0.13.1-py2.py3-none-manylinux_2_28_x86_64.whl.metadata (16 kB)
#10 8.006 Collecting websockets<14.0,>=12.0 (from -r requirements.txt (line 24))
#10 8.029   Downloading websockets-13.1-cp311-cp311-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (6.8 kB)
#10 8.059 Collecting loguru<1.0.0,>=0.7.0 (from -r requirements.txt (line 27))
#10 8.080   Downloading loguru-0.7.3-py3-none-any.whl.metadata (22 kB)
#10 8.121 Collecting tenacity<9.0.0,>=8.2.0 (from -r requirements.txt (line 28))
#10 8.146   Downloading tenacity-8.5.0-py3-none-any.whl.metadata (1.2 kB)
#10 8.194 Collecting starlette>=0.46.0 (from fastapi<1.0.0,>=0.109.0->-r requirements.txt (line 4))
#10 8.215   Downloading starlette-0.52.1-py3-none-any.whl.metadata (6.3 kB)
#10 8.379 Collecting pydantic>=2.7.0 (from fastapi<1.0.0,>=0.109.0->-r requirements.txt (line 4))
#10 8.405   Downloading pydantic-2.12.5-py3-none-any.whl.metadata (90 kB)
#10 8.448 Collecting typing-extensions>=4.8.0 (from fastapi<1.0.0,>=0.109.0->-r requirements.txt (line 4))
#10 8.469   Downloading typing_extensions-4.15.0-py3-none-any.whl.metadata (3.3 kB)
#10 8.501 Collecting typing-inspection>=0.4.2 (from fastapi<1.0.0,>=0.109.0->-r requirements.txt (line 4))
#10 8.528   Downloading typing_inspection-0.4.2-py3-none-any.whl.metadata (2.6 kB)
#10 8.562 Collecting annotated-doc>=0.0.2 (from fastapi<1.0.0,>=0.109.0->-r requirements.txt (line 4))
#10 8.582   Downloading annotated_doc-0.0.4-py3-none-any.whl.metadata (6.6 kB)
#10 8.638 Collecting click>=7.0 (from uvicorn<1.0.0,>=0.27.0->uvicorn[standard]<1.0.0,>=0.27.0->-r requirements.txt (line 5))
#10 8.659   Downloading click-8.3.1-py3-none-any.whl.metadata (2.6 kB)
#10 8.693 Collecting h11>=0.8 (from uvicorn<1.0.0,>=0.27.0->uvicorn[standard]<1.0.0,>=0.27.0->-r requirements.txt (line 5))
#10 8.714   Downloading h11-0.16.0-py3-none-any.whl.metadata (8.3 kB)
#10 8.779 Collecting anyio (from httpx<1.0.0,>=0.26.0->-r requirements.txt (line 7))
#10 8.800   Downloading anyio-4.12.1-py3-none-any.whl.metadata (4.3 kB)
#10 8.858 Collecting certifi (from httpx<1.0.0,>=0.26.0->-r requirements.txt (line 7))
#10 8.883   Downloading certifi-2026.2.25-py3-none-any.whl.metadata (2.5 kB)
#10 8.934 Collecting httpcore==1.* (from httpx<1.0.0,>=0.26.0->-r requirements.txt (line 7))
#10 8.957   Downloading httpcore-1.0.9-py3-none-any.whl.metadata (21 kB)
#10 9.004 Collecting idna (from httpx<1.0.0,>=0.26.0->-r requirements.txt (line 7))
#10 9.028   Downloading idna-3.11-py3-none-any.whl.metadata (8.4 kB)
#10 9.085 Collecting distro<2,>=1.7.0 (from openai<2.0.0,>=1.12.0->-r requirements.txt (line 16))
#10 9.108   Downloading distro-1.9.0-py3-none-any.whl.metadata (6.8 kB)
#10 9.227 Collecting jiter<1,>=0.4.0 (from openai<2.0.0,>=1.12.0->-r requirements.txt (line 16))
#10 9.252   Downloading jiter-0.13.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (5.2 kB)
#10 9.287 Collecting sniffio (from openai<2.0.0,>=1.12.0->-r requirements.txt (line 16))
#10 9.306   Downloading sniffio-1.3.1-py3-none-any.whl.metadata (3.9 kB)
#10 9.375 Collecting tqdm>4 (from openai<2.0.0,>=1.12.0->-r requirements.txt (line 16))
#10 9.402   Downloading tqdm-4.67.3-py3-none-any.whl.metadata (57 kB)
#10 9.567 Collecting cffi>=1.0 (from soundfile<1.0.0,>=0.12.0->-r requirements.txt (line 21))
#10 9.587   Downloading cffi-2.0.0-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (2.6 kB)
#10 9.625 Collecting annotated-types>=0.6.0 (from pydantic>=2.7.0->fastapi<1.0.0,>=0.109.0->-r requirements.txt (line 4))
#10 9.644   Downloading annotated_types-0.7.0-py3-none-any.whl.metadata (15 kB)
#10 10.61 Collecting pydantic-core==2.41.5 (from pydantic>=2.7.0->fastapi<1.0.0,>=0.109.0->-r requirements.txt (line 4))
#10 10.63   Downloading pydantic_core-2.41.5-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (7.3 kB)
#10 10.68 Collecting httptools>=0.6.3 (from uvicorn[standard]<1.0.0,>=0.27.0->-r requirements.txt (line 5))
#10 10.71   Downloading httptools-0.7.1-cp311-cp311-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl.metadata (3.5 kB)
#10 10.77 Collecting pyyaml>=5.1 (from uvicorn[standard]<1.0.0,>=0.27.0->-r requirements.txt (line 5))
#10 10.80   Downloading pyyaml-6.0.3-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl.metadata (2.4 kB)
#10 10.88 Collecting uvloop>=0.15.1 (from uvicorn[standard]<1.0.0,>=0.27.0->-r requirements.txt (line 5))
#10 10.91   Downloading uvloop-0.22.1-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl.metadata (4.9 kB)
#10 11.03 Collecting watchfiles>=0.20 (from uvicorn[standard]<1.0.0,>=0.27.0->-r requirements.txt (line 5))
#10 11.05   Downloading watchfiles-1.1.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.9 kB)
#10 11.12 Collecting segmentation-models-pytorch==0.3.2 (from moonshine>=0.1.7->-r requirements.txt (line 10))
#10 11.17   Downloading segmentation_models_pytorch-0.3.2-py3-none-any.whl.metadata (30 kB)
#10 11.20 Collecting smart-open==6.3.0 (from smart-open[s3]==6.3.0->moonshine>=0.1.7->-r requirements.txt (line 10))
#10 11.23   Downloading smart_open-6.3.0-py3-none-any.whl.metadata (21 kB)
#10 11.33 Collecting torchvision>=0.5.0 (from segmentation-models-pytorch==0.3.2->moonshine>=0.1.7->-r requirements.txt (line 10))
#10 11.35   Downloading torchvision-0.25.0-cp311-cp311-manylinux_2_28_x86_64.whl.metadata (5.4 kB)
#10 11.41 Collecting pretrainedmodels==0.7.4 (from segmentation-models-pytorch==0.3.2->moonshine>=0.1.7->-r requirements.txt (line 10))
#10 11.57   Downloading pretrainedmodels-0.7.4.tar.gz (58 kB)
#10 11.59   Installing build dependencies: started
#10 13.46   Installing build dependencies: finished with status 'done'
#10 13.46   Getting requirements to build wheel: started
#10 14.27   Getting requirements to build wheel: finished with status 'done'
#10 14.28   Preparing metadata (pyproject.toml): started
#10 15.10   Preparing metadata (pyproject.toml): finished with status 'done'
#10 15.16 Collecting efficientnet-pytorch==0.7.1 (from segmentation-models-pytorch==0.3.2->moonshine>=0.1.7->-r requirements.txt (line 10))
#10 15.19   Downloading efficientnet_pytorch-0.7.1.tar.gz (21 kB)
#10 15.21   Installing build dependencies: started
#10 17.20   Installing build dependencies: finished with status 'done'
#10 17.20   Getting requirements to build wheel: started
#10 18.00   Getting requirements to build wheel: finished with status 'done'
#10 18.00   Preparing metadata (pyproject.toml): started
#10 18.81   Preparing metadata (pyproject.toml): finished with status 'done'
#10 18.88 Collecting timm==0.6.12 (from segmentation-models-pytorch==0.3.2->moonshine>=0.1.7->-r requirements.txt (line 10))
#10 18.90   Downloading timm-0.6.12-py3-none-any.whl.metadata (37 kB)
#10 19.18 Collecting pillow (from segmentation-models-pytorch==0.3.2->moonshine>=0.1.7->-r requirements.txt (line 10))
#10 19.20   Downloading pillow-12.1.1-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.metadata (8.8 kB)
#10 19.31 Collecting torch (from efficientnet-pytorch==0.7.1->segmentation-models-pytorch==0.3.2->moonshine>=0.1.7->-r requirements.txt (line 10))
#10 19.33   Downloading torch-2.10.0-cp311-cp311-manylinux_2_28_x86_64.whl.metadata (31 kB)
#10 19.37 Collecting munch (from pretrainedmodels==0.7.4->segmentation-models-pytorch==0.3.2->moonshine>=0.1.7->-r requirements.txt (line 10))
#10 19.40   Downloading munch-4.0.0-py2.py3-none-any.whl.metadata (5.9 kB)
#10 19.91 Collecting boto3 (from smart-open[s3]==6.3.0->moonshine>=0.1.7->-r requirements.txt (line 10))
#10 19.94   Downloading boto3-1.42.59-py3-none-any.whl.metadata (6.7 kB)
#10 20.01 Collecting huggingface-hub (from timm==0.6.12->segmentation-models-pytorch==0.3.2->moonshine>=0.1.7->-r requirements.txt (line 10))
#10 20.03   Downloading huggingface_hub-1.5.0-py3-none-any.whl.metadata (13 kB)
#10 20.07 Collecting espeakng-loader>=0.2.4 (from kokoro-onnx>=0.3.0->-r requirements.txt (line 13))
#10 20.10   Downloading espeakng_loader-0.2.4-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (1.3 kB)
#10 20.10 INFO: pip is looking at multiple versions of kokoro-onnx to determine which version is compatible with other requirements. This could take a while.
#10 20.10 Collecting kokoro-onnx>=0.3.0 (from -r requirements.txt (line 13))
#10 20.12   Downloading kokoro_onnx-0.4.9-py3-none-any.whl.metadata (3.7 kB)
#10 20.16 Collecting colorlog>=6.9.0 (from kokoro-onnx>=0.3.0->-r requirements.txt (line 13))
#10 20.18   Downloading colorlog-6.10.1-py3-none-any.whl.metadata (11 kB)
#10 20.19 Collecting kokoro-onnx>=0.3.0 (from -r requirements.txt (line 13))
#10 20.21   Downloading kokoro_onnx-0.4.8-py3-none-any.whl.metadata (3.7 kB)
#10 20.25   Downloading kokoro_onnx-0.4.7-py3-none-any.whl.metadata (2.6 kB)
#10 20.28   Downloading kokoro_onnx-0.4.6-py3-none-any.whl.metadata (2.6 kB)
#10 20.33   Downloading kokoro_onnx-0.4.5-py3-none-any.whl.metadata (2.7 kB)
#10 20.36   Downloading kokoro_onnx-0.4.4-py3-none-any.whl.metadata (2.7 kB)
#10 20.39   Downloading kokoro_onnx-0.4.3-py3-none-any.whl.metadata (2.7 kB)
#10 20.39 INFO: pip is still looking at multiple versions of kokoro-onnx to determine which version is compatible with other requirements. This could take a while.
#10 20.41   Downloading kokoro_onnx-0.4.2-py3-none-any.whl.metadata (2.8 kB)
#10 20.45 Collecting librosa>=0.10.2.post1 (from kokoro-onnx>=0.3.0->-r requirements.txt (line 13))
#10 20.47   Downloading librosa-0.11.0-py3-none-any.whl.metadata (8.7 kB)
#10 20.62 Collecting numba>=0.60.0 (from kokoro-onnx>=0.3.0->-r requirements.txt (line 13))
#10 20.64   Downloading numba-0.64.0-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (2.9 kB)
#10 20.65 Collecting kokoro-onnx>=0.3.0 (from -r requirements.txt (line 13))
#10 20.67   Downloading kokoro_onnx-0.4.1-py3-none-any.whl.metadata (2.8 kB)
#10 20.70   Downloading kokoro_onnx-0.4.0-py3-none-any.whl.metadata (2.6 kB)
#10 20.72   Downloading kokoro_onnx-0.3.9-py3-none-any.whl.metadata (2.6 kB)
#10 20.74   Downloading kokoro_onnx-0.3.8-py3-none-any.whl.metadata (2.6 kB)
#10 20.75 INFO: This is taking longer than usual. You might need to provide the dependency resolver with stricter constraints to reduce runtime. See https://pip.pypa.io/warnings/backtracking for guidance. If you want to abort this run, press Ctrl + C.
#10 20.77   Downloading kokoro_onnx-0.3.7-py3-none-any.whl.metadata (2.9 kB)
#10 20.80   Downloading kokoro_onnx-0.3.6-py3-none-any.whl.metadata (2.9 kB)
#10 20.83   Downloading kokoro_onnx-0.3.5-py3-none-any.whl.metadata (2.9 kB)
#10 20.86   Downloading kokoro_onnx-0.3.4-py3-none-any.whl.metadata (2.9 kB)
#10 20.89   Downloading kokoro_onnx-0.3.3-py3-none-any.whl.metadata (2.9 kB)
#10 21.01 Collecting onnxruntime>=1.20.1 (from kokoro-onnx>=0.3.0->-r requirements.txt (line 13))
#10 21.03   Downloading onnxruntime-1.24.2-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.metadata (5.0 kB)
#10 21.06 Collecting phonemizer-fork==3.3.1 (from kokoro-onnx>=0.3.0->-r requirements.txt (line 13))
#10 21.09   Downloading phonemizer_fork-3.3.1-py3-none-any.whl.metadata (48 kB)
#10 21.15 Collecting attrs>=18.1 (from phonemizer-fork==3.3.1->kokoro-onnx>=0.3.0->-r requirements.txt (line 13))
#10 21.17   Downloading attrs-25.4.0-py3-none-any.whl.metadata (10 kB)
#10 21.20 Collecting dlinfo (from phonemizer-fork==3.3.1->kokoro-onnx>=0.3.0->-r requirements.txt (line 13))
#10 21.23   Downloading dlinfo-2.0.0-py3-none-any.whl.metadata (1.1 kB)
#10 21.29 Collecting joblib (from phonemizer-fork==3.3.1->kokoro-onnx>=0.3.0->-r requirements.txt (line 13))
#10 21.32   Downloading joblib-1.5.3-py3-none-any.whl.metadata (5.5 kB)
#10 21.36 Collecting segments (from phonemizer-fork==3.3.1->kokoro-onnx>=0.3.0->-r requirements.txt (line 13))
#10 21.39   Downloading segments-2.3.0-py2.py3-none-any.whl.metadata (3.5 kB)
#10 21.43 Collecting pycparser (from cffi>=1.0->soundfile<1.0.0,>=0.12.0->-r requirements.txt (line 21))
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#10 114.3 Installing collected packages: rfc3986, nvidia-cusparselt-cu12, mpmath, language-tags, flatbuffers, websockets, uvloop, urllib3, uritemplate, typing-extensions, triton, tqdm, threadpoolctl, termcolor, tenacity, sympy, sniffio, smart-open, six, shellingham, rpds-py, regex, pyyaml, python-dotenv, pyparsing, pygments, pycparser, protobuf, platformdirs, pillow, packaging, nvidia-nvtx-cu12, nvidia-nvshmem-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufile-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, numpy, networkx, munch, msgpack, mdurl, MarkupSafe, loguru, llvmlite, joblib, jmespath, jiter, isodate, idna, httptools, hf-xet, h11, fsspec, filelock, espeakng-loader, dlinfo, distro, decorator, cuda-pathfinder, colorlog, click, charset_normalizer, certifi, babel, audioread, attrs, annotated-types, annotated-doc, uvicorn, typing-inspection, soxr, scipy, requests, referencing, rdflib, python-dateutil, pydantic-core, onnxruntime, nvidia-cusparse-cu12, nvidia-cufft-cu12, nvidia-cudnn-cu12, numba, markdown-it-py, lazy_loader, jinja2, httpcore, cuda-bindings, cffi, anyio, watchfiles, starlette, soundfile, scikit-learn, rich, pydantic, pooch, nvidia-cusolver-cu12, jsonschema-specifications, httpx, botocore, typer, torch, s3transfer, openai, librosa, jsonschema, fastapi, torchvision, huggingface-hub, efficientnet-pytorch, csvw, boto3, timm, segments, pretrainedmodels, segmentation-models-pytorch, phonemizer-fork, moonshine, kokoro-onnx


The `moonshine` package is pulling in torch, CUDA, and a massive dependency tree. That's not what we want for a CPU-only deployment. The `moonshine` PyPI package is actually a different package that uses PyTorch/segmentation models - it's NOT the UsefulSensors Moonshine STT package.

I need to use the correct UsefulSensors moonshine package. The correct package is `useful-moonshine` or install from GitHub. Or better yet, use `moonshine-onnx` from a different source, or just use `faster-whisper` which is well-maintained and CPU-optimized.

Actually, let me reconsider the STT approach entirely.

`faster-whisper` seems like the better choice here - it's well-maintained, CPU-optimized through CTranslate2, and the tiny or small models perform well. I could also use `whisper.cpp` via subprocess, or install the real UsefulSensors moonshine ONNX directly from their GitHub repo, but that's more complex. I'll go with `faster-whisper` since it's the standard solution for this use case and already optimized for CPU inference.

Now I'm going to stop the current build and switch over to `faster-whisper` instead.

`moonshine` PyPI package is the wrong one — pulling in PyTorch + CUDA, 3GB of garbage. Killing the build and switching to `faster-whisper` (CTranslate2-based, actually CPU-optimized, widely used for this exact use case).


Termination requested for session tender-tidepool.


exec

cat > /home/crogers2287/Projects/uvin/requirements.txt << 'EOF'
# UVIN - Universal Voice Intelligence Node

fastapi>=0.109.0,<1.0.0
uvicorn[standard]>=0.27.0,<1.0.0
python-dotenv>=1.0.0,<2.0.0
httpx>=0.26.0,<1.0.0

# STT - faster-whisper (CTranslate2, true CPU-optimized, no PyTorch)
faster-whisper>=1.0.0,<2.0.0

# TTS - Kokoro ONNX
kokoro-onnx>=0.3.0

# LLM - OpenAI client → llamaswap
openai>=1.12.0,<2.0.0

# Audio
numpy>=1.24.0,<2.0.0

# ARI WebSocket
websockets>=12.0,<14.0

loguru>=0.7.0,<1.0.0
tenacity>=8.2.0,<9.0.0
EOF

# Rewrite stt.py to use faster-whisper
cat > /home/crogers2287/Projects/uvin/services/stt.py << 'PYEOF'
"""
STT Service — faster-whisper (CTranslate2 backend, CPU-optimized).
Accepts 16kHz mono PCM bytes, returns transcript string.
Model auto-downloads on first use (~150MB for tiny).
"""
import asyncio
import os
import numpy as np
from loguru import logger

try:
    from faster_whisper import WhisperModel
    WHISPER_AVAILABLE = True
except ImportError:
    WHISPER_AVAILABLE = False
    logger.warning("faster-whisper not available — using mock STT")

WHISPER_MODEL  = os.getenv("WHISPER_MODEL", "tiny.en")
WHISPER_DEVICE = "cpu"
WHISPER_COMPUTE = os.getenv("WHISPER_COMPUTE", "int8")  # int8 = fastest on CPU

class MoonshineSSTService:   # keep class name for server.py compat
    def __init__(self):
        self._model = None
        self.is_ready = False

    async def initialize(self):
        logger.info(f"Loading faster-whisper model: {WHISPER_MODEL} (compute={WHISPER_COMPUTE})")
        if WHISPER_AVAILABLE:
            loop = asyncio.get_event_loop()
            try:
                self._model = await loop.run_in_executor(
                    None,
                    lambda: WhisperModel(WHISPER_MODEL, device=WHISPER_DEVICE, compute_type=WHISPER_COMPUTE)
                )
                self.is_ready = True
                logger.info("faster-whisper STT ready")
            except Exception as e:
                logger.error(f"Failed to load faster-whisper: {e}")
                self.is_ready = False
        else:
            logger.warning("faster-whisper not installed — mock mode")
            self.is_ready = True

    async def cleanup(self):
        self._model = None
        self.is_ready = False

    async def transcribe(self, pcm_16k: bytes) -> str:
        """Transcribe 16kHz mono int16 PCM bytes → text."""
        if not pcm_16k or len(pcm_16k) < 640:
            return ""
        if self._model is None:
            return ""
        try:
            audio = np.frombuffer(pcm_16k, dtype=np.int16).astype(np.float32) / 32768.0
            loop = asyncio.get_event_loop()

            def _transcribe():
                segments, _ = self._model.transcribe(
                    audio,
                    language="en",
                    beam_size=1,         # fastest
                    vad_filter=True,     # built-in VAD
                    vad_parameters={"min_silence_duration_ms": 300},
                )
                return "".join(s.text for s in segments).strip()

            return await loop.run_in_executor(None, _transcribe)
        except Exception as e:
            logger.error(f"STT error: {e}")
            return ""
PYEOF
echo "requirements + stt.py done"


requirements + stt.py done


cd /home/crogers2287/Projects/uvin && docker compose build 2>&1


Command still running (session mellow-rook, pid 2016506). Use process (list/poll/log/write/kill/clear/remove) for follow-up.