Output:
Exit code: 0
Wall time: 75.9 seconds
Output:
Defaulting to user installation because normal site-packages is not writeable
Looking in indexes: https://mcache-dsm.massedcompute.com/simple, https://pypi.org/simple
Collecting omnilingual-asr
  Downloading omnilingual_asr-0.2.0-py3-none-any.whl.metadata (16 kB)
Collecting fairseq2<=0.6.0,>=0.5.2 (from fairseq2[arrow]<=0.6.0,>=0.5.2->omnilingual-asr)
  Downloading fairseq2-0.6-py3-none-any.whl.metadata (1.8 kB)
Requirement already satisfied: pyarrow>=20.0.0 in /home/ubuntu/.local/lib/python3.10/site-packages (from omnilingual-asr) (22.0.0)
Requirement already satisfied: torch in /home/ubuntu/.local/lib/python3.10/site-packages (from omnilingual-asr) (2.9.0)
Requirement already satisfied: torchaudio in /home/ubuntu/.local/lib/python3.10/site-packages (from omnilingual-asr) (2.9.0)
Requirement already satisfied: numba in /home/ubuntu/.local/lib/python3.10/site-packages (from omnilingual-asr) (0.61.2)
Requirement already satisfied: pandas in /home/ubuntu/.local/lib/python3.10/site-packages (from omnilingual-asr) (2.3.3)
Requirement already satisfied: numpy in /home/ubuntu/.local/lib/python3.10/site-packages (from omnilingual-asr) (1.26.4)
Collecting kenlm (from omnilingual-asr)
  Downloading kenlm-0.3.0.tar.gz (427 kB)
  Installing build dependencies: started
  Installing build dependencies: finished with status 'done'
  Getting requirements to build wheel: started
  Getting requirements to build wheel: finished with status 'done'
  Preparing metadata (pyproject.toml): started
  Preparing metadata (pyproject.toml): finished with status 'done'
Collecting polars>=1.29.0 (from omnilingual-asr)
  Downloading polars-1.37.1-py3-none-any.whl.metadata (10 kB)
Requirement already satisfied: editdistance~=0.8 in /home/ubuntu/.local/lib/python3.10/site-packages (from fairseq2<=0.6.0,>=0.5.2->fairseq2[arrow]<=0.6.0,>=0.5.2->omnilingual-asr) (0.8.1)
Collecting fairseq2n==0.6 (from fairseq2<=0.6.0,>=0.5.2->fairseq2[arrow]<=0.6.0,>=0.5.2->omnilingual-asr)
  Downloading fairseq2n-0.6-cp310-cp310-manylinux_2_28_x86_64.whl.metadata (1.1 kB)
Requirement already satisfied: huggingface_hub~=0.32 in /home/ubuntu/.local/lib/python3.10/site-packages (from fairseq2<=0.6.0,>=0.5.2->fairseq2[arrow]<=0.6.0,>=0.5.2->omnilingual-asr) (0.36.0)
Collecting importlib_metadata~=7.0 (from fairseq2<=0.6.0,>=0.5.2->fairseq2[arrow]<=0.6.0,>=0.5.2->omnilingual-asr)
  Downloading importlib_metadata-7.2.1-py3-none-any.whl.metadata (4.6 kB)
Requirement already satisfied: importlib_resources~=6.4 in /home/ubuntu/.local/lib/python3.10/site-packages (from fairseq2<=0.6.0,>=0.5.2->fairseq2[arrow]<=0.6.0,>=0.5.2->omnilingual-asr) (6.5.2)
Requirement already satisfied: mypy-extensions~=1.0 in /home/ubuntu/.local/lib/python3.10/site-packages (from fairseq2<=0.6.0,>=0.5.2->fairseq2[arrow]<=0.6.0,>=0.5.2->omnilingual-asr) (1.1.0)
Collecting packaging~=24.1 (from fairseq2<=0.6.0,>=0.5.2->fairseq2[arrow]<=0.6.0,>=0.5.2->omnilingual-asr)
  Downloading packaging-24.2-py3-none-any.whl.metadata (3.2 kB)
Requirement already satisfied: psutil~=5.9 in /usr/local/lib/python3.10/dist-packages (from fairseq2<=0.6.0,>=0.5.2->fairseq2[arrow]<=0.6.0,>=0.5.2->omnilingual-asr) (5.9.6)
Requirement already satisfied: ruamel.yaml~=0.18 in /home/ubuntu/.local/lib/python3.10/site-packages (from fairseq2<=0.6.0,>=0.5.2->fairseq2[arrow]<=0.6.0,>=0.5.2->omnilingual-asr) (0.18.16)
Collecting rich~=13.7 (from fairseq2<=0.6.0,>=0.5.2->fairseq2[arrow]<=0.6.0,>=0.5.2->omnilingual-asr)
  Downloading rich-13.9.4-py3-none-any.whl.metadata (18 kB)
Collecting sacrebleu~=2.4 (from fairseq2<=0.6.0,>=0.5.2->fairseq2[arrow]<=0.6.0,>=0.5.2->omnilingual-asr)
  Downloading sacrebleu-2.6.0-py3-none-any.whl.metadata (39 kB)
Requirement already satisfied: safetensors~=0.6 in /home/ubuntu/.local/lib/python3.10/site-packages (from fairseq2<=0.6.0,>=0.5.2->fairseq2[arrow]<=0.6.0,>=0.5.2->omnilingual-asr) (0.7.0)
Requirement already satisfied: tiktoken~=0.7 in /home/ubuntu/.local/lib/python3.10/site-packages (from fairseq2<=0.6.0,>=0.5.2->fairseq2[arrow]<=0.6.0,>=0.5.2->omnilingual-asr) (0.12.0)
Collecting torcheval~=0.0.6 (from fairseq2<=0.6.0,>=0.5.2->fairseq2[arrow]<=0.6.0,>=0.5.2->omnilingual-asr)
  Downloading torcheval-0.0.7-py3-none-any.whl.metadata (8.6 kB)
Requirement already satisfied: tqdm~=4.62 in /home/ubuntu/.local/lib/python3.10/site-packages (from fairseq2<=0.6.0,>=0.5.2->fairseq2[arrow]<=0.6.0,>=0.5.2->omnilingual-asr) (4.67.1)
Requirement already satisfied: typing_extensions~=4.12 in /home/ubuntu/.local/lib/python3.10/site-packages (from fairseq2<=0.6.0,>=0.5.2->fairseq2[arrow]<=0.6.0,>=0.5.2->omnilingual-asr) (4.15.0)
Collecting blobfile~=3.0.0 (from fairseq2<=0.6.0,>=0.5.2->fairseq2[arrow]<=0.6.0,>=0.5.2->omnilingual-asr)
  Downloading blobfile-3.0.0-py3-none-any.whl.metadata (15 kB)
Collecting tbb>=2021.8 (from fairseq2n==0.6->fairseq2<=0.6.0,>=0.5.2->fairseq2[arrow]<=0.6.0,>=0.5.2->omnilingual-asr)
  Downloading tbb-2022.3.0-py2.py3-none-manylinux_2_28_x86_64.whl.metadata (1.0 kB)
Collecting torch (from omnilingual-asr)
  Using cached torch-2.8.0-cp310-cp310-manylinux_2_28_x86_64.whl.metadata (30 kB)
Requirement already satisfied: filelock in /home/ubuntu/.local/lib/python3.10/site-packages (from torch->omnilingual-asr) (3.20.0)
Requirement already satisfied: sympy>=1.13.3 in /home/ubuntu/.local/lib/python3.10/site-packages (from torch->omnilingual-asr) (1.14.0)
Requirement already satisfied: networkx in /home/ubuntu/.local/lib/python3.10/site-packages (from torch->omnilingual-asr) (3.4.2)
Requirement already satisfied: jinja2 in /home/ubuntu/.local/lib/python3.10/site-packages (from torch->omnilingual-asr) (3.1.6)
Requirement already satisfied: fsspec in /home/ubuntu/.local/lib/python3.10/site-packages (from torch->omnilingual-asr) (2023.10.0)
Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.8.93 in /home/ubuntu/.local/lib/python3.10/site-packages (from torch->omnilingual-asr) (12.8.93)
Requirement already satisfied: nvidia-cuda-runtime-cu12==12.8.90 in /home/ubuntu/.local/lib/python3.10/site-packages (from torch->omnilingual-asr) (12.8.90)
Requirement already satisfied: nvidia-cuda-cupti-cu12==12.8.90 in /home/ubuntu/.local/lib/python3.10/site-packages (from torch->omnilingual-asr) (12.8.90)
Requirement already satisfied: nvidia-cudnn-cu12==9.10.2.21 in /home/ubuntu/.local/lib/python3.10/site-packages (from torch->omnilingual-asr) (9.10.2.21)
Requirement already satisfied: nvidia-cublas-cu12==12.8.4.1 in /home/ubuntu/.local/lib/python3.10/site-packages (from torch->omnilingual-asr) (12.8.4.1)
Requirement already satisfied: nvidia-cufft-cu12==11.3.3.83 in /home/ubuntu/.local/lib/python3.10/site-packages (from torch->omnilingual-asr) (11.3.3.83)
Requirement already satisfied: nvidia-curand-cu12==10.3.9.90 in /home/ubuntu/.local/lib/python3.10/site-packages (from torch->omnilingual-asr) (10.3.9.90)
Requirement already satisfied: nvidia-cusolver-cu12==11.7.3.90 in /home/ubuntu/.local/lib/python3.10/site-packages (from torch->omnilingual-asr) (11.7.3.90)
Requirement already satisfied: nvidia-cusparse-cu12==12.5.8.93 in /home/ubuntu/.local/lib/python3.10/site-packages (from torch->omnilingual-asr) (12.5.8.93)
Requirement already satisfied: nvidia-cusparselt-cu12==0.7.1 in /home/ubuntu/.local/lib/python3.10/site-packages (from torch->omnilingual-asr) (0.7.1)
Collecting nvidia-nccl-cu12==2.27.3 (from torch->omnilingual-asr)
  Using cached nvidia_nccl_cu12-2.27.3-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (2.0 kB)
Requirement already satisfied: nvidia-nvtx-cu12==12.8.90 in /home/ubuntu/.local/lib/python3.10/site-packages (from torch->omnilingual-asr) (12.8.90)
Requirement already satisfied: nvidia-nvjitlink-cu12==12.8.93 in /home/ubuntu/.local/lib/python3.10/site-packages (from torch->omnilingual-asr) (12.8.93)
Requirement already satisfied: nvidia-cufile-cu12==1.13.1.3 in /home/ubuntu/.local/lib/python3.10/site-packages (from torch->omnilingual-asr) (1.13.1.3)
Collecting triton==3.4.0 (from torch->omnilingual-asr)
  Using cached triton-3.4.0-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.metadata (1.7 kB)
Requirement already satisfied: setuptools>=40.8.0 in /home/ubuntu/.local/lib/python3.10/site-packages (from triton==3.4.0->torch->omnilingual-asr) (69.5.1)
Collecting pycryptodomex>=3.8 (from blobfile~=3.0.0->fairseq2<=0.6.0,>=0.5.2->fairseq2[arrow]<=0.6.0,>=0.5.2->omnilingual-asr)
  Downloading pycryptodomex-3.23.0-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (3.4 kB)
Requirement already satisfied: urllib3<3,>=1.25.3 in /home/ubuntu/.local/lib/python3.10/site-packages (from blobfile~=3.0.0->fairseq2<=0.6.0,>=0.5.2->fairseq2[arrow]<=0.6.0,>=0.5.2->omnilingual-asr) (2.6.2)
Requirement already satisfied: lxml>=4.9 in /home/ubuntu/.local/lib/python3.10/site-packages (from blobfile~=3.0.0->fairseq2<=0.6.0,>=0.5.2->fairseq2[arrow]<=0.6.0,>=0.5.2->omnilingual-asr) (6.0.2)
Collecting retrying~=1.3.4 (from fairseq2[arrow]<=0.6.0,>=0.5.2->omnilingual-asr)
  Downloading retrying-1.3.7-py3-none-any.whl.metadata (7.3 kB)
Requirement already satisfied: xxhash~=3.5 in /home/ubuntu/.local/lib/python3.10/site-packages (from fairseq2[arrow]<=0.6.0,>=0.5.2->omnilingual-asr) (3.6.0)
Requirement already satisfied: pyyaml>=5.1 in /usr/lib/python3/dist-packages (from huggingface_hub~=0.32->fairseq2<=0.6.0,>=0.5.2->fairseq2[arrow]<=0.6.0,>=0.5.2->omnilingual-asr) (5.4.1)
Requirement already satisfied: requests in /home/ubuntu/.local/lib/python3.10/site-packages (from huggingface_hub~=0.32->fairseq2<=0.6.0,>=0.5.2->fairseq2[arrow]<=0.6.0,>=0.5.2->omnilingual-asr) (2.32.5)
Requirement already satisfied: hf-xet<2.0.0,>=1.1.3 in /home/ubuntu/.local/lib/python3.10/site-packages (from huggingface_hub~=0.32->fairseq2<=0.6.0,>=0.5.2->fairseq2[arrow]<=0.6.0,>=0.5.2->omnilingual-asr) (1.2.0)
Requirement already satisfied: zipp>=0.5 in /usr/lib/python3/dist-packages (from importlib_metadata~=7.0->fairseq2<=0.6.0,>=0.5.2->fairseq2[arrow]<=0.6.0,>=0.5.2->omnilingual-asr) (1.0.0)
Requirement already satisfied: markdown-it-py>=2.2.0 in /home/ubuntu/.local/lib/python3.10/site-packages (from rich~=13.7->fairseq2<=0.6.0,>=0.5.2->fairseq2[arrow]<=0.6.0,>=0.5.2->omnilingual-asr) (4.0.0)
Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /home/ubuntu/.local/lib/python3.10/site-packages (from rich~=13.7->fairseq2<=0.6.0,>=0.5.2->fairseq2[arrow]<=0.6.0,>=0.5.2->omnilingual-asr) (2.19.2)
Requirement already satisfied: ruamel.yaml.clib>=0.2.7 in /home/ubuntu/.local/lib/python3.10/site-packages (from ruamel.yaml~=0.18->fairseq2<=0.6.0,>=0.5.2->fairseq2[arrow]<=0.6.0,>=0.5.2->omnilingual-asr) (0.2.15)
Requirement already satisfied: portalocker in /home/ubuntu/.local/lib/python3.10/site-packages (from sacrebleu~=2.4->fairseq2<=0.6.0,>=0.5.2->fairseq2[arrow]<=0.6.0,>=0.5.2->omnilingual-asr) (2.10.1)
Requirement already satisfied: regex in /home/ubuntu/.local/lib/python3.10/site-packages (from sacrebleu~=2.4->fairseq2<=0.6.0,>=0.5.2->fairseq2[arrow]<=0.6.0,>=0.5.2->omnilingual-asr) (2025.11.3)
Requirement already satisfied: tabulate>=0.8.9 in /home/ubuntu/.local/lib/python3.10/site-packages (from sacrebleu~=2.4->fairseq2<=0.6.0,>=0.5.2->fairseq2[arrow]<=0.6.0,>=0.5.2->omnilingual-asr) (0.8.10)
Requirement already satisfied: colorama in /usr/lib/python3/dist-packages (from sacrebleu~=2.4->fairseq2<=0.6.0,>=0.5.2->fairseq2[arrow]<=0.6.0,>=0.5.2->omnilingual-asr) (0.4.4)
Requirement already satisfied: mdurl~=0.1 in /home/ubuntu/.local/lib/python3.10/site-packages (from markdown-it-py>=2.2.0->rich~=13.7->fairseq2<=0.6.0,>=0.5.2->fairseq2[arrow]<=0.6.0,>=0.5.2->omnilingual-asr) (0.1.2)
Collecting polars-runtime-32==1.37.1 (from polars>=1.29.0->omnilingual-asr)
  Downloading polars_runtime_32-1.37.1-cp310-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (1.5 kB)
Requirement already satisfied: charset_normalizer<4,>=2 in /home/ubuntu/.local/lib/python3.10/site-packages (from requests->huggingface_hub~=0.32->fairseq2<=0.6.0,>=0.5.2->fairseq2[arrow]<=0.6.0,>=0.5.2->omnilingual-asr) (3.4.4)
Requirement already satisfied: idna<4,>=2.5 in /usr/lib/python3/dist-packages (from requests->huggingface_hub~=0.32->fairseq2<=0.6.0,>=0.5.2->fairseq2[arrow]<=0.6.0,>=0.5.2->omnilingual-asr) (3.3)
Requirement already satisfied: certifi>=2017.4.17 in /usr/lib/python3/dist-packages (from requests->huggingface_hub~=0.32->fairseq2<=0.6.0,>=0.5.2->fairseq2[arrow]<=0.6.0,>=0.5.2->omnilingual-asr) (2020.6.20)
Requirement already satisfied: mpmath<1.4,>=1.1.0 in /home/ubuntu/.local/lib/python3.10/site-packages (from sympy>=1.13.3->torch->omnilingual-asr) (1.3.0)
Collecting tcmlib==1.* (from tbb>=2021.8->fairseq2n==0.6->fairseq2<=0.6.0,>=0.5.2->fairseq2[arrow]<=0.6.0,>=0.5.2->omnilingual-asr)
  Downloading tcmlib-1.4.1-py2.py3-none-manylinux_2_28_x86_64.whl.metadata (962 bytes)
Requirement already satisfied: MarkupSafe>=2.0 in /home/ubuntu/.local/lib/python3.10/site-packages (from jinja2->torch->omnilingual-asr) (3.0.3)
Requirement already satisfied: llvmlite<0.45,>=0.44.0dev0 in /home/ubuntu/.local/lib/python3.10/site-packages (from numba->omnilingual-asr) (0.44.0)
Requirement already satisfied: python-dateutil>=2.8.2 in /home/ubuntu/.local/lib/python3.10/site-packages (from pandas->omnilingual-asr) (2.9.0.post0)
Requirement already satisfied: pytz>=2020.1 in /home/ubuntu/.local/lib/python3.10/site-packages (from pandas->omnilingual-asr) (2025.2)
Requirement already satisfied: tzdata>=2022.7 in /home/ubuntu/.local/lib/python3.10/site-packages (from pandas->omnilingual-asr) (2025.3)
Requirement already satisfied: six>=1.5 in /home/ubuntu/.local/lib/python3.10/site-packages (from python-dateutil>=2.8.2->pandas->omnilingual-asr) (1.17.0)
INFO: pip is looking at multiple versions of torchaudio to determine which version is compatible with other requirements. This could take a while.
Collecting torchaudio (from omnilingual-asr)
  Using cached torchaudio-2.9.1-cp310-cp310-manylinux_2_28_x86_64.whl.metadata (6.9 kB)
  Using cached torchaudio-2.8.0-cp310-cp310-manylinux_2_28_x86_64.whl.metadata (7.2 kB)
Downloading omnilingual_asr-0.2.0-py3-none-any.whl (85 kB)
Downloading fairseq2-0.6-py3-none-any.whl (548 kB)
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Downloading fairseq2n-0.6-cp310-cp310-manylinux_2_28_x86_64.whl (3.2 MB)
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Using cached torch-2.8.0-cp310-cp310-manylinux_2_28_x86_64.whl (888.0 MB)
Using cached nvidia_nccl_cu12-2.27.3-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (322.4 MB)
Using cached triton-3.4.0-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (155.4 MB)
Downloading blobfile-3.0.0-py3-none-any.whl (75 kB)
Downloading importlib_metadata-7.2.1-py3-none-any.whl (25 kB)
Downloading packaging-24.2-py3-none-any.whl (65 kB)
Downloading retrying-1.3.7-py3-none-any.whl (11 kB)
Downloading rich-13.9.4-py3-none-any.whl (242 kB)
Downloading sacrebleu-2.6.0-py3-none-any.whl (100 kB)
Downloading torcheval-0.0.7-py3-none-any.whl (179 kB)
Downloading polars-1.37.1-py3-none-any.whl (805 kB)
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Downloading polars_runtime_32-1.37.1-cp310-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (45.1 MB)
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Downloading pycryptodomex-3.23.0-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.3 MB)
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Downloading tbb-2022.3.0-py2.py3-none-manylinux_2_28_x86_64.whl (4.2 MB)
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Downloading tcmlib-1.4.1-py2.py3-none-manylinux_2_28_x86_64.whl (2.7 MB)
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Using cached torchaudio-2.8.0-cp310-cp310-manylinux_2_28_x86_64.whl (4.0 MB)
Building wheels for collected packages: kenlm
  Building wheel for kenlm (pyproject.toml): started
  Building wheel for kenlm (pyproject.toml): finished with status 'done'
  Created wheel for kenlm: filename=kenlm-0.3.0-cp310-cp310-linux_x86_64.whl size=3171515 sha256=85c7e00c8fce3b045cfd0103f360ef8f7875bed82b6bafa1e192b7b394f523a8
  Stored in directory: /home/ubuntu/.cache/pip/wheels/86/57/70/f69b85d0dd1368f139ea278de9eb67b45977d00dd70b7674de
Successfully built kenlm
Installing collected packages: tcmlib, retrying, kenlm, triton, torcheval, tbb, sacrebleu, pycryptodomex, polars-runtime-32, packaging, nvidia-nccl-cu12, importlib_metadata, rich, polars, blobfile, torch, torchaudio, fairseq2n, fairseq2, omnilingual-asr
  Attempting uninstall: retrying
    Found existing installation: retrying 1.4.2
    Uninstalling retrying-1.4.2:
      Successfully uninstalled retrying-1.4.2
  Attempting uninstall: triton
    Found existing installation: triton 3.5.0
    Uninstalling triton-3.5.0:
      Successfully uninstalled triton-3.5.0
  Attempting uninstall: packaging
    Found existing installation: packaging 25.0
    Uninstalling packaging-25.0:
      Successfully uninstalled packaging-25.0
  Attempting uninstall: nvidia-nccl-cu12
    Found existing installation: nvidia-nccl-cu12 2.27.5
    Uninstalling nvidia-nccl-cu12-2.27.5:
      Successfully uninstalled nvidia-nccl-cu12-2.27.5
  Attempting uninstall: rich
    Found existing installation: rich 14.2.0
    Uninstalling rich-14.2.0:
      Successfully uninstalled rich-14.2.0
  Attempting uninstall: torch
    Found existing installation: torch 2.9.0
    Uninstalling torch-2.9.0:
      Successfully uninstalled torch-2.9.0
  Attempting uninstall: torchaudio
    Found existing installation: torchaudio 2.9.0
    Uninstalling torchaudio-2.9.0:
      Successfully uninstalled torchaudio-2.9.0

ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
azure-cli-core 2.81.0 requires argcomplete~=3.5.2, but you have argcomplete 3.5.1 which is incompatible.
deepfilternet 0.5.6 requires packaging<24.0,>=23.0, but you have packaging 24.2 which is incompatible.
gradio 4.8.0 requires markupsafe~=2.0, but you have markupsafe 3.0.3 which is incompatible.
gradio 4.8.0 requires tomlkit==0.12.0, but you have tomlkit 0.13.3 which is incompatible.
gradio-client 0.7.1 requires websockets<12.0,>=10.0, but you have websockets 15.0.1 which is incompatible.
hydra-core 1.3.2 requires antlr4-python3-runtime==4.9.*, but you have antlr4-python3-runtime 4.13.2 which is incompatible.
resemble-enhance 0.0.1 requires deepspeed==0.12.4, but you have deepspeed 0.18.3 which is incompatible.
resemble-enhance 0.0.1 requires numpy==1.26.2, but you have numpy 1.26.4 which is incompatible.
resemble-enhance 0.0.1 requires pandas==2.1.3, but you have pandas 2.3.3 which is incompatible.
resemble-enhance 0.0.1 requires rich==13.7.0, but you have rich 13.9.4 which is incompatible.
resemble-enhance 0.0.1 requires scipy==1.11.4, but you have scipy 1.15.3 which is incompatible.
resemble-enhance 0.0.1 requires torch==2.1.1, but you have torch 2.8.0 which is incompatible.
resemble-enhance 0.0.1 requires torchaudio==2.1.1, but you have torchaudio 2.8.0 which is incompatible.
resemble-enhance 0.0.1 requires torchvision==0.16.1, but you have torchvision 0.24.0 which is incompatible.
resemble-enhance 0.0.1 requires tqdm==4.66.1, but you have tqdm 4.67.1 which is incompatible.
torchvision 0.24.0 requires torch==2.9.0, but you have torch 2.8.0 which is incompatible.
vastai 0.5.0 requires python-dateutil==2.6.1, but you have python-dateutil 2.9.0.post0 which is incompatible.
vllm 0.13.0 requires torch==2.9.0, but you have torch 2.8.0 which is incompatible.
vllm 0.13.0 requires torchaudio==2.9.0, but you have torchaudio 2.8.0 which is incompatible.
Successfully installed blobfile-3.0.0 fairseq2-0.6 fairseq2n-0.6 importlib_metadata-7.2.1 kenlm-0.3.0 nvidia-nccl-cu12-2.27.3 omnilingual-asr-0.2.0 packaging-24.2 polars-1.37.1 polars-runtime-32-1.37.1 pycryptodomex-3.23.0 retrying-1.3.7 rich-13.9.4 sacrebleu-2.6.0 tbb-2022.3.0 tcmlib-1.4.1 torch-2.8.0 torchaudio-2.8.0 torcheval-0.0.7 triton-3.4.0


Summary: auto

## Skills
A skill is a set of local instructions to follow that is stored in a `SKILL.md` file. Below is the list of skills that can be used. Each entry includes a name, description, and file path so you can open the source for full instructions when using a specific skill.
### Available skills
- skill-creator: Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Codex's capabilities with specialized knowledge, workflows, or tool integrations. (file: /home/ubuntu/.codex/skills/.system/skill-creator/SKILL.md)
- skill-installer: Install Codex skills into $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: /home/ubuntu/.codex/skills/.system/skill-installer/SKILL.md)
### How to use skills
- Discovery: The list above is the skills available in this session (name + description + file path). Skill bodies live on disk at the listed paths.
- Trigger rules: If the user names a skill (with `$SkillName` or plain text) OR the task clearly matches a skill's description shown above, you must use that skill for that turn. Multiple mentions mean use them all. Do not carry skills across turns unless re-mentioned.
- Missing/blocked: If a named skill isn't in the list or the path can't be read, say so briefly and continue with the best fallback.
- How to use a skill (progressive disclosure):
  1) After deciding to use a skill, open its `SKILL.md`. Read only enough to follow the workflow.
  2) If `SKILL.md` points to extra folders such as `references/`, load only the specific files needed for the request; don't bulk-load everything.
  3) If `scripts/` exist, prefer running or patching them instead of retyping large code blocks.
  4) If `assets/` or templates exist, reuse them instead of recreating from scratch.
- Coordination and sequencing:
  - If multiple skills apply, choose the minimal set that covers the request and state the order you'll use them.
  - Announce which skill(s) you're using and why (one short line). If you skip an obvious skill, say why.
- Context hygiene:
  - Keep context small: summarize long sections instead of pasting them; only load extra files when needed.
  - Avoid deep reference-chasing: prefer opening only files directly linked from `SKILL.md` unless you're blocked.
  - When variants exist (frameworks, providers, domains), pick only the relevant reference file(s) and note that choice.
- Safety and fallback: If a skill can't be applied cleanly (missing files, unclear instructions), state the issue, pick the next-best approach, and continue.


**Alerting about torch downgrade risk**

The torch downgrade to 2.8.0 conflicts with vllm’s 2.9.0 requirement and may break parts of the pipeline; I'll notify the user and pause for instructions before proceeding.


**Confirming environment change and next steps**

Installed omnilingual-asr downgraded torch and torchaudio to 2.8.0, causing a version conflict with vllm which requires 2.9.0; I'll inform the user and ask whether to proceed with the current setup, roll back, or isolate environments before updating the plan and continuing.


Clone omnilingual-asr repo and review README for required inference steps/model naming

Integrate or script runs for Seamless-M4T v2 large and Omnilingual-ASR largest on data/bench/maya_hf_v2_100, producing results.jsonl + eval.json

Add per-language WER metrics for the new models into results.md table


Output: Plan updated


Summary: auto


I hit an environment change while installing `omnilingual-asr` that affects the existing setup:

- `pip install omnilingual-asr` downgraded `torch` to 2.8.0 and `torchaudio` to 2.8.0.
- That breaks `vllm`’s pinned requirement (`torch==2.9.0`, `torchaudio==2.9.0`), so Voxtral/Gemma vLLM runs may no longer work until we restore.

How do you want to proceed?

1) Proceed with the new evaluations in the current environment (torch 2.8.0), then restore torch/torchaudio 2.9.0 afterward.
2) Create an isolated venv for omnilingual-asr + Seamless M4T, run those there, and keep the base env intact for vLLM.
3) Roll back now to torch/torchaudio 2.9.0 and skip omnilingual-asr unless you want a separate machine/env.

I already cloned the repo to `external/omnilingual-asr` and confirmed the largest model target should be `omniASR_LLM_7B_v2` (supports our language codes like `hin_Deva`, `asm_Beng`, etc.).


# Context from my IDE setup:

## Open tabs:
- results.md: results.md
- asr.py: maya_transcribe/asr.py
- .env: .env
- Prompt.md: Prompt.md
- eval.json: outputs/bench/maya_hf_hin_v2/gemma3n_e2b_vllm/eval.json

## My request for Codex:
fine create a new venv and run with it if you feel like it breaks the global env. else proceed with evals.
Create an isolated venv for omnilingual-asr + Seamless M4T, run those there, and keep the base env intact for vLLM.


Output:
Exit code: 0
Wall time: 2.6 seconds
Output:


. .venv_omni/bin/activate && python -m pip install --upgrade pip


Output:
Exit code: 0
Wall time: 1.7 seconds
Output:
Looking in indexes: https://mcache-dsm.massedcompute.com/simple, https://pypi.org/simple
Requirement already satisfied: pip in ./.venv_omni/lib/python3.10/site-packages (22.0.2)
Collecting pip
  Using cached pip-25.3-py3-none-any.whl (1.8 MB)
Installing collected packages: pip
  Attempting uninstall: pip
    Found existing installation: pip 22.0.2
    Uninstalling pip-22.0.2:
      Successfully uninstalled pip-22.0.2
Successfully installed pip-25.3


. .venv_omni/bin/activate && python -m pip install "omnilingual-asr" "transformers" "accelerate" "sentencepiece" "soundfile"


Output:
Exit code: 0
Wall time: 95 seconds
Output:
Looking in indexes: https://mcache-dsm.massedcompute.com/simple, https://pypi.org/simple
Collecting omnilingual-asr
  Using cached omnilingual_asr-0.2.0-py3-none-any.whl.metadata (16 kB)
Collecting transformers
  Downloading transformers-4.57.5-py3-none-any.whl.metadata (43 kB)
Collecting accelerate
  Using cached accelerate-1.12.0-py3-none-any.whl.metadata (19 kB)
Collecting sentencepiece
  Using cached sentencepiece-0.2.1-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.metadata (10 kB)
Collecting soundfile
  Using cached soundfile-0.13.1-py2.py3-none-manylinux_2_28_x86_64.whl.metadata (16 kB)
Collecting fairseq2<=0.6.0,>=0.5.2 (from fairseq2[arrow]<=0.6.0,>=0.5.2->omnilingual-asr)
  Using cached fairseq2-0.6-py3-none-any.whl.metadata (1.8 kB)
Collecting pyarrow>=20.0.0 (from omnilingual-asr)
  Using cached pyarrow-22.0.0-cp310-cp310-manylinux_2_28_x86_64.whl.metadata (3.2 kB)
Collecting torch (from omnilingual-asr)
  Using cached torch-2.9.1-cp310-cp310-manylinux_2_28_x86_64.whl.metadata (30 kB)
Collecting torchaudio (from omnilingual-asr)
  Using cached torchaudio-2.9.1-cp310-cp310-manylinux_2_28_x86_64.whl.metadata (6.9 kB)
Collecting numba (from omnilingual-asr)
  Using cached numba-0.63.1-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (2.9 kB)
Collecting pandas (from omnilingual-asr)
  Using cached pandas-2.3.3-cp310-cp310-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl.metadata (91 kB)
Collecting numpy (from omnilingual-asr)
  Using cached numpy-2.2.6-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (62 kB)
Collecting kenlm (from omnilingual-asr)
  Using cached kenlm-0.3.0-cp310-cp310-linux_x86_64.whl
Collecting polars>=1.29.0 (from omnilingual-asr)
  Using cached polars-1.37.1-py3-none-any.whl.metadata (10 kB)
Collecting editdistance~=0.8 (from fairseq2<=0.6.0,>=0.5.2->fairseq2[arrow]<=0.6.0,>=0.5.2->omnilingual-asr)
  Using cached editdistance-0.8.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (3.9 kB)
Collecting fairseq2n==0.6 (from fairseq2<=0.6.0,>=0.5.2->fairseq2[arrow]<=0.6.0,>=0.5.2->omnilingual-asr)
  Using cached fairseq2n-0.6-cp310-cp310-manylinux_2_28_x86_64.whl.metadata (1.1 kB)
Collecting huggingface_hub~=0.32 (from fairseq2<=0.6.0,>=0.5.2->fairseq2[arrow]<=0.6.0,>=0.5.2->omnilingual-asr)
  Using cached huggingface_hub-0.36.0-py3-none-any.whl.metadata (14 kB)
Collecting importlib_metadata~=7.0 (from fairseq2<=0.6.0,>=0.5.2->fairseq2[arrow]<=0.6.0,>=0.5.2->omnilingual-asr)
  Using cached importlib_metadata-7.2.1-py3-none-any.whl.metadata (4.6 kB)
Collecting importlib_resources~=6.4 (from fairseq2<=0.6.0,>=0.5.2->fairseq2[arrow]<=0.6.0,>=0.5.2->omnilingual-asr)
  Using cached importlib_resources-6.5.2-py3-none-any.whl.metadata (3.9 kB)
Collecting mypy-extensions~=1.0 (from fairseq2<=0.6.0,>=0.5.2->fairseq2[arrow]<=0.6.0,>=0.5.2->omnilingual-asr)
  Using cached mypy_extensions-1.1.0-py3-none-any.whl.metadata (1.1 kB)
Collecting numpy (from omnilingual-asr)
  Using cached numpy-1.26.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (61 kB)
Collecting packaging~=24.1 (from fairseq2<=0.6.0,>=0.5.2->fairseq2[arrow]<=0.6.0,>=0.5.2->omnilingual-asr)
  Using cached packaging-24.2-py3-none-any.whl.metadata (3.2 kB)
Collecting psutil~=5.9 (from fairseq2<=0.6.0,>=0.5.2->fairseq2[arrow]<=0.6.0,>=0.5.2->omnilingual-asr)
  Downloading psutil-5.9.8-cp36-abi3-manylinux_2_12_x86_64.manylinux2010_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (21 kB)
Collecting ruamel.yaml~=0.18 (from fairseq2<=0.6.0,>=0.5.2->fairseq2[arrow]<=0.6.0,>=0.5.2->omnilingual-asr)
  Downloading ruamel_yaml-0.19.1-py3-none-any.whl.metadata (16 kB)
Collecting rich~=13.7 (from fairseq2<=0.6.0,>=0.5.2->fairseq2[arrow]<=0.6.0,>=0.5.2->omnilingual-asr)
  Using cached rich-13.9.4-py3-none-any.whl.metadata (18 kB)
Collecting sacrebleu~=2.4 (from fairseq2<=0.6.0,>=0.5.2->fairseq2[arrow]<=0.6.0,>=0.5.2->omnilingual-asr)
  Using cached sacrebleu-2.6.0-py3-none-any.whl.metadata (39 kB)
Collecting safetensors~=0.6 (from fairseq2<=0.6.0,>=0.5.2->fairseq2[arrow]<=0.6.0,>=0.5.2->omnilingual-asr)
  Using cached safetensors-0.7.0-cp38-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.1 kB)
Collecting tiktoken~=0.7 (from fairseq2<=0.6.0,>=0.5.2->fairseq2[arrow]<=0.6.0,>=0.5.2->omnilingual-asr)
  Using cached tiktoken-0.12.0-cp310-cp310-manylinux_2_28_x86_64.whl.metadata (6.7 kB)
Collecting torcheval~=0.0.6 (from fairseq2<=0.6.0,>=0.5.2->fairseq2[arrow]<=0.6.0,>=0.5.2->omnilingual-asr)
  Using cached torcheval-0.0.7-py3-none-any.whl.metadata (8.6 kB)
Collecting tqdm~=4.62 (from fairseq2<=0.6.0,>=0.5.2->fairseq2[arrow]<=0.6.0,>=0.5.2->omnilingual-asr)
  Using cached tqdm-4.67.1-py3-none-any.whl.metadata (57 kB)
Collecting typing_extensions~=4.12 (from fairseq2<=0.6.0,>=0.5.2->fairseq2[arrow]<=0.6.0,>=0.5.2->omnilingual-asr)
  Using cached typing_extensions-4.15.0-py3-none-any.whl.metadata (3.3 kB)
Collecting blobfile~=3.0.0 (from fairseq2<=0.6.0,>=0.5.2->fairseq2[arrow]<=0.6.0,>=0.5.2->omnilingual-asr)
  Using cached blobfile-3.0.0-py3-none-any.whl.metadata (15 kB)
Collecting tbb>=2021.8 (from fairseq2n==0.6->fairseq2<=0.6.0,>=0.5.2->fairseq2[arrow]<=0.6.0,>=0.5.2->omnilingual-asr)
  Using cached tbb-2022.3.0-py2.py3-none-manylinux_2_28_x86_64.whl.metadata (1.0 kB)
Collecting torch (from omnilingual-asr)
  Using cached torch-2.8.0-cp310-cp310-manylinux_2_28_x86_64.whl.metadata (30 kB)
Collecting filelock (from torch->omnilingual-asr)
  Downloading filelock-3.20.3-py3-none-any.whl.metadata (2.1 kB)
Collecting sympy>=1.13.3 (from torch->omnilingual-asr)
  Using cached sympy-1.14.0-py3-none-any.whl.metadata (12 kB)
Collecting networkx (from torch->omnilingual-asr)
  Using cached networkx-3.4.2-py3-none-any.whl.metadata (6.3 kB)
Collecting jinja2 (from torch->omnilingual-asr)
  Using cached jinja2-3.1.6-py3-none-any.whl.metadata (2.9 kB)
Collecting fsspec (from torch->omnilingual-asr)
  Downloading fsspec-2026.1.0-py3-none-any.whl.metadata (10 kB)
Collecting nvidia-cuda-nvrtc-cu12==12.8.93 (from torch->omnilingual-asr)
  Using cached nvidia_cuda_nvrtc_cu12-12.8.93-py3-none-manylinux2010_x86_64.manylinux_2_12_x86_64.whl.metadata (1.7 kB)
Collecting nvidia-cuda-runtime-cu12==12.8.90 (from torch->omnilingual-asr)
  Using cached nvidia_cuda_runtime_cu12-12.8.90-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (1.7 kB)
Collecting nvidia-cuda-cupti-cu12==12.8.90 (from torch->omnilingual-asr)
  Using cached nvidia_cuda_cupti_cu12-12.8.90-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (1.7 kB)
Collecting nvidia-cudnn-cu12==9.10.2.21 (from torch->omnilingual-asr)
  Using cached nvidia_cudnn_cu12-9.10.2.21-py3-none-manylinux_2_27_x86_64.whl.metadata (1.8 kB)
Collecting nvidia-cublas-cu12==12.8.4.1 (from torch->omnilingual-asr)
  Using cached nvidia_cublas_cu12-12.8.4.1-py3-none-manylinux_2_27_x86_64.whl.metadata (1.7 kB)
Collecting nvidia-cufft-cu12==11.3.3.83 (from torch->omnilingual-asr)
  Using cached nvidia_cufft_cu12-11.3.3.83-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (1.7 kB)
Collecting nvidia-curand-cu12==10.3.9.90 (from torch->omnilingual-asr)
  Using cached nvidia_curand_cu12-10.3.9.90-py3-none-manylinux_2_27_x86_64.whl.metadata (1.7 kB)
Collecting nvidia-cusolver-cu12==11.7.3.90 (from torch->omnilingual-asr)
  Using cached nvidia_cusolver_cu12-11.7.3.90-py3-none-manylinux_2_27_x86_64.whl.metadata (1.8 kB)
Collecting nvidia-cusparse-cu12==12.5.8.93 (from torch->omnilingual-asr)
  Using cached nvidia_cusparse_cu12-12.5.8.93-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (1.8 kB)
Collecting nvidia-cusparselt-cu12==0.7.1 (from torch->omnilingual-asr)
  Using cached nvidia_cusparselt_cu12-0.7.1-py3-none-manylinux2014_x86_64.whl.metadata (7.0 kB)
Collecting nvidia-nccl-cu12==2.27.3 (from torch->omnilingual-asr)
  Using cached nvidia_nccl_cu12-2.27.3-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (2.0 kB)
Collecting nvidia-nvtx-cu12==12.8.90 (from torch->omnilingual-asr)
  Using cached nvidia_nvtx_cu12-12.8.90-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (1.8 kB)
Collecting nvidia-nvjitlink-cu12==12.8.93 (from torch->omnilingual-asr)
  Using cached nvidia_nvjitlink_cu12-12.8.93-py3-none-manylinux2010_x86_64.manylinux_2_12_x86_64.whl.metadata (1.7 kB)
Collecting nvidia-cufile-cu12==1.13.1.3 (from torch->omnilingual-asr)
  Using cached nvidia_cufile_cu12-1.13.1.3-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (1.7 kB)
Collecting triton==3.4.0 (from torch->omnilingual-asr)
  Using cached triton-3.4.0-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.metadata (1.7 kB)
Requirement already satisfied: setuptools>=40.8.0 in ./.venv_omni/lib/python3.10/site-packages (from triton==3.4.0->torch->omnilingual-asr) (59.6.0)
Collecting pycryptodomex>=3.8 (from blobfile~=3.0.0->fairseq2<=0.6.0,>=0.5.2->fairseq2[arrow]<=0.6.0,>=0.5.2->omnilingual-asr)
  Using cached pycryptodomex-3.23.0-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (3.4 kB)
Collecting urllib3<3,>=1.25.3 (from blobfile~=3.0.0->fairseq2<=0.6.0,>=0.5.2->fairseq2[arrow]<=0.6.0,>=0.5.2->omnilingual-asr)
  Using cached urllib3-2.6.3-py3-none-any.whl.metadata (6.9 kB)
Collecting lxml>=4.9 (from blobfile~=3.0.0->fairseq2<=0.6.0,>=0.5.2->fairseq2[arrow]<=0.6.0,>=0.5.2->omnilingual-asr)
  Using cached lxml-6.0.2-cp310-cp310-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl.metadata (3.6 kB)
Collecting retrying~=1.3.4 (from fairseq2[arrow]<=0.6.0,>=0.5.2->omnilingual-asr)
  Using cached retrying-1.3.7-py3-none-any.whl.metadata (7.3 kB)
Collecting xxhash~=3.5 (from fairseq2[arrow]<=0.6.0,>=0.5.2->omnilingual-asr)
  Using cached xxhash-3.6.0-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl.metadata (13 kB)
Collecting pyyaml>=5.1 (from huggingface_hub~=0.32->fairseq2<=0.6.0,>=0.5.2->fairseq2[arrow]<=0.6.0,>=0.5.2->omnilingual-asr)
  Using cached pyyaml-6.0.3-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl.metadata (2.4 kB)
Collecting requests (from huggingface_hub~=0.32->fairseq2<=0.6.0,>=0.5.2->fairseq2[arrow]<=0.6.0,>=0.5.2->omnilingual-asr)
  Using cached requests-2.32.5-py3-none-any.whl.metadata (4.9 kB)
Collecting hf-xet<2.0.0,>=1.1.3 (from huggingface_hub~=0.32->fairseq2<=0.6.0,>=0.5.2->fairseq2[arrow]<=0.6.0,>=0.5.2->omnilingual-asr)
  Using cached hf_xet-1.2.0-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.9 kB)
Collecting zipp>=0.5 (from importlib_metadata~=7.0->fairseq2<=0.6.0,>=0.5.2->fairseq2[arrow]<=0.6.0,>=0.5.2->omnilingual-asr)
  Using cached zipp-3.23.0-py3-none-any.whl.metadata (3.6 kB)
Collecting markdown-it-py>=2.2.0 (from rich~=13.7->fairseq2<=0.6.0,>=0.5.2->fairseq2[arrow]<=0.6.0,>=0.5.2->omnilingual-asr)
  Using cached markdown_it_py-4.0.0-py3-none-any.whl.metadata (7.3 kB)
Collecting pygments<3.0.0,>=2.13.0 (from rich~=13.7->fairseq2<=0.6.0,>=0.5.2->fairseq2[arrow]<=0.6.0,>=0.5.2->omnilingual-asr)
  Using cached pygments-2.19.2-py3-none-any.whl.metadata (2.5 kB)
Collecting portalocker (from sacrebleu~=2.4->fairseq2<=0.6.0,>=0.5.2->fairseq2[arrow]<=0.6.0,>=0.5.2->omnilingual-asr)
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Installing collected packages: tcmlib, retrying, pytz, nvidia-cusparselt-cu12, mpmath, kenlm, zipp, xxhash, urllib3, tzdata, typing_extensions, triton, tqdm, tbb, tabulate, sympy, six, sentencepiece, safetensors, ruamel.yaml, regex, pyyaml, pygments, pycryptodomex, pycparser, pyarrow, psutil, portalocker, polars-runtime-32, packaging, nvidia-nvtx-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, mypy-extensions, mdurl, MarkupSafe, lxml, llvmlite, importlib_resources, idna, hf-xet, fsspec, filelock, editdistance, colorama, charset_normalizer, certifi, torcheval, sacrebleu, requests, python-dateutil, polars, nvidia-cusparse-cu12, nvidia-cufft-cu12, nvidia-cudnn-cu12, numba, markdown-it-py, jinja2, importlib_metadata, cffi, blobfile, tiktoken, soundfile, rich, pandas, nvidia-cusolver-cu12, huggingface_hub, torch, tokenizers, transformers, torchaudio, fairseq2n, accelerate, fairseq2, omnilingual-asr

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. .venv_omni/bin/activate && python - <<'PY'
from omnilingual_asr.models.wav2vec2_llama.lang_ids import supported_langs
langs = ['eng_Latn','hin_Deva','ben_Beng','guj_Gujr','kan_Knda','mal_Mlym','mar_Deva','ory_Orya','pan_Guru','tam_Taml','tel_Telu','asm_Beng']
missing = [l for l in langs if l not in supported_langs]
print('missing', missing)
PY


Output:
Exit code: 0
Wall time: 4.3 seconds
Output:
missing []


. .venv_omni/bin/activate && python - <<'PY'
import json
from collections import Counter
from pathlib import Path

refs_path = Path('data/bench/maya_hf_v2_100/refs.jsonl')
lang_map = {
    'asm': 'asm_Beng',
    'ben': 'ben_Beng',
    'eng': 'eng_Latn',
    'guj': 'guj_Gujr',
    'hin': 'hin_Deva',
    'kan': 'kan_Knda',
    'mal': 'mal_Mlym',
    'mar': 'mar_Deva',
    'ory': 'ory_Orya',
    'pan': 'pan_Guru',
    'tam': 'tam_Taml',
    'tel': 'tel_Telu',
}
rows = []
counts = Counter()
for line in refs_path.read_text(encoding='utf-8').splitlines():
    if not line.strip():
        continue
    row = json.loads(line)
    lang = row.get('lang')
    if lang not in lang_map:
        continue
    audio_rel = row.get('audio_relpath')
    if not audio_rel:
        continue
    audio_path = str(Path('data/bench/maya_hf_v2_100/audio') / audio_rel)
    rows.append({'segment_id': row['segment_id'], 'lang': lang_map[lang], 'audio_path': audio_path})
    counts[lang] += 1

out = Path('outputs/bench/maya_hf_v2_100/omniasr_7b_v2_inputs.json')
out.parent.mkdir(parents=True, exist_ok=True)
out.write_text(json.dumps(rows, ensure_ascii=False, indent=2) + '\n', encoding='utf-8')
print('wrote', out, 'rows', len(rows))
print('counts', dict(counts))
PY


Output:
Exit code: 0
Wall time: 0.1 seconds
Output:
wrote outputs/bench/maya_hf_v2_100/omniasr_7b_v2_inputs.json rows 1200
counts {'asm': 100, 'ben': 100, 'eng': 100, 'guj': 100, 'hin': 100, 'kan': 100, 'mal': 100, 'mar': 100, 'ory': 100, 'pan': 100, 'tam': 100, 'tel': 100}


. .venv_omni/bin/activate && python - <<'PY'
import json
from pathlib import Path
from omnilingual_asr.models.inference.pipeline import ASRInferencePipeline

input_path = Path('outputs/bench/maya_hf_v2_100/omniasr_7b_v2_inputs.json')
rows = json.loads(input_path.read_text(encoding='utf-8'))

out_dir = Path('outputs/bench/maya_hf_v2_100/omniasr_llm_7b_v2')
out_dir.mkdir(parents=True, exist_ok=True)
results_path = out_dir / 'results.jsonl'

pipeline = ASRInferencePipeline(model_card='omniASR_LLM_7B_v2')

batch_size = 4
with results_path.open('w', encoding='utf-8') as f:
    for i in range(0, len(rows), batch_size):
        batch = rows[i:i+batch_size]
        audio_files = [r['audio_path'] for r in batch]
        langs = [r['lang'] for r in batch]
        texts = pipeline.transcribe(audio_files, lang=langs, batch_size=len(batch))
        for r, text in zip(batch, texts):
            rec = {
                'segment_id': r['segment_id'],
                'asr': {
                    'omniasr_llm_7b_v2': {
                        'model': 'omniASR_LLM_7B_v2',
                        'text': text,
                        'language': r['lang'],
                        'language_probability': None,
                        'segments': [],
                        'meta': {},
                    }
                },
                'final': {'text': text, 'source': 'asr:omniasr_llm_7b_v2'},
            }
            f.write(json.dumps(rec, ensure_ascii=False) + '\n')
        if (i // batch_size) % 20 == 0:
            print(f'processed {i+len(batch)}/{len(rows)}')

print('done', results_path)
PY


Output:
Exit code: 1
Wall time: 220.9 seconds
Output:

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  5%|▌         | 1.58G/29.1G [00:31<07:20, 67.0MB/s]
  5%|▌         | 1.58G/29.1G [00:31<06:53, 71.3MB/s]
  5%|▌         | 1.59G/29.1G [00:31<06:25, 76.5MB/s]
  6%|▌         | 1.60G/29.1G [00:31<06:50, 71.9MB/s]
  6%|▌         | 1.61G/29.1G [00:31<07:16, 67.6MB/s]
  6%|▌         | 1.61G/29.1G [00:31<07:37, 64.5MB/s]
  6%|▌         | 1.62G/29.1G [00:32<07:46, 63.2MB/s]
  6%|▌         | 1.63G/29.1G [00:32<07:47, 63.1MB/s]
  6%|▌         | 1.63G/29.1G [00:32<07:19, 67.1MB/s]
  6%|▌         | 1.64G/29.1G [00:32<07:00, 70.0MB/s]
  6%|▌         | 1.65G/29.1G [00:32<07:21, 66.7MB/s]
  6%|▌         | 1.65G/29.1G [00:32<07:53, 62.2MB/s]
  6%|▌         | 1.66G/29.1G [00:32<07:28, 65.7MB/s]
  6%|▌         | 1.67G/29.1G [00:32<07:44, 63.4MB/s]
  6%|▌         | 1.67G/29.1G [00:32<08:06, 60.5MB/s]
  6%|▌         | 1.68G/29.1G [00:33<09:13, 53.2MB/s]
  6%|▌         | 1.68G/29.1G [00:33<09:04, 54.0MB/s]
  6%|▌         | 1.69G/29.1G [00:33<09:04, 54.0MB/s]
  6%|▌         | 1.70G/29.1G [00:33<08:31, 57.5MB/s]
  6%|▌         | 1.70G/29.1G [00:33<07:49, 62.7MB/s]
  6%|▌         | 1.71G/29.1G [00:33<10:23, 47.1MB/s]
  6%|▌         | 1.71G/29.1G [00:33<10:02, 48.7MB/s]
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  6%|▌         | 1.73G/29.1G [00:34<09:29, 51.6MB/s]
  6%|▌         | 1.73G/29.1G [00:34<08:23, 58.3MB/s]
  6%|▌         | 1.74G/29.1G [00:34<08:23, 58.3MB/s]
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  6%|▌         | 1.78G/29.1G [00:35<13:30, 36.2MB/s]
  6%|▌         | 1.79G/29.1G [00:35<08:05, 60.3MB/s]
  6%|▌         | 1.80G/29.1G [00:35<07:01, 69.4MB/s]
  6%|▌         | 1.81G/29.1G [00:35<06:41, 73.0MB/s]
  6%|▋         | 1.82G/29.1G [00:35<06:41, 73.0MB/s]
  6%|▋         | 1.83G/29.1G [00:35<08:37, 56.6MB/s]
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  6%|▋         | 1.84G/29.1G [00:36<08:15, 59.0MB/s]
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  6%|▋         | 1.87G/29.1G [00:36<07:06, 68.5MB/s]
  6%|▋         | 1.87G/29.1G [00:36<07:19, 66.5MB/s]
  6%|▋         | 1.88G/29.1G [00:36<06:47, 71.6MB/s]
  6%|▋         | 1.89G/29.1G [00:36<07:04, 68.8MB/s]
  7%|▋         | 1.90G/29.1G [00:37<07:47, 62.5MB/s]
  7%|▋         | 1.90G/29.1G [00:37<07:54, 61.5MB/s]
  7%|▋         | 1.91G/29.1G [00:37<07:47, 62.3MB/s]
  7%|▋         | 1.92G/29.1G [00:37<08:58, 54.1MB/s]
  7%|▋         | 1.92G/29.1G [00:37<10:18, 47.1MB/s]
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  7%|▋         | 1.93G/29.1G [00:37<09:17, 52.3MB/s]
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  7%|▋         | 1.94G/29.1G [00:38<08:48, 55.1MB/s]
  7%|▋         | 1.95G/29.1G [00:38<08:05, 60.0MB/s]
  7%|▋         | 1.96G/29.1G [00:38<08:27, 57.4MB/s]
  7%|▋         | 1.96G/29.1G [00:38<08:28, 57.3MB/s]
  7%|▋         | 1.97G/29.1G [00:38<08:38, 56.1MB/s]
  7%|▋         | 1.98G/29.1G [00:38<07:59, 60.7MB/s]
  7%|▋         | 1.98G/29.1G [00:38<07:58, 60.8MB/s]
  7%|▋         | 1.99G/29.1G [00:38<08:05, 59.9MB/s]
  7%|▋         | 1.99G/29.1G [00:38<07:47, 62.3MB/s]
  7%|▋         | 2.00G/29.1G [00:39<07:08, 67.9MB/s]
  7%|▋         | 2.01G/29.1G [00:39<07:54, 61.3MB/s]
  7%|▋         | 2.02G/29.1G [00:39<08:23, 57.7MB/s]
  7%|▋         | 2.02G/29.1G [00:39<14:33, 33.2MB/s]
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  7%|▋         | 2.12G/29.1G [00:41<07:33, 63.8MB/s]
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  8%|▊       …15619 tokens truncated…<06:34, 54.6MB/s]
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Traceback (most recent call last):
  File "<stdin>", line 12, in <module>
  File "/home/ubuntu/maya-transcribe/.venv_omni/lib/python3.10/site-packages/omnilingual_asr/models/inference/pipeline.py", line 201, in __init__
    self.model = load_model(model_card, device=self.device, dtype=self.dtype)
  File "/home/ubuntu/maya-transcribe/.venv_omni/lib/python3.10/site-packages/fairseq2/models/hub.py", line 717, in load_model
    return global_loader.load(card, gangs, device, dtype, config, mmap, progress)
  File "/home/ubuntu/maya-transcribe/.venv_omni/lib/python3.10/site-packages/fairseq2/models/hub.py", line 769, in load
    return family.load_model(card, gangs, dtype, config, mmap, progress)
  File "/home/ubuntu/maya-transcribe/.venv_omni/lib/python3.10/site-packages/fairseq2/models/family.py", line 340, in load_model
    path = self._asset_download_manager.download_model(uri, name, progress=progress)
  File "/home/ubuntu/maya-transcribe/.venv_omni/lib/python3.10/site-packages/fairseq2/assets/download_manager.py", line 141, in download_model
    return manager.download_model(uri, model_name, force=force, progress=progress)
  File "/home/ubuntu/maya-transcribe/.venv_omni/lib/python3.10/site-packages/fairseq2/assets/download_manager.py", line 360, in download_model
    return op.run()
  File "/home/ubuntu/maya-transcribe/.venv_omni/lib/python3.10/site-packages/fairseq2/assets/download_manager.py", line 440, in run
    self._download_asset()
  File "/home/ubuntu/maya-transcribe/.venv_omni/lib/python3.10/site-packages/fairseq2/assets/download_manager.py", line 624, in _download_asset
    fp.write(buffer)
  File "/usr/lib/python3.10/tempfile.py", line 638, in func_wrapper
    return func(*args, **kwargs)
OSError: [Errno 28] No space left on device


**Planning disk usage check and user prompt**

I'll run disk usage and cache size commands to report current space, then ask the user how to proceed with freeing space—offering to clean cache folders only after getting explicit permission.


Output:
Exit code: 0
Wall time: 0 seconds
Output:
Filesystem      Size  Used Avail Use% Mounted on
/dev/sda2       984G  932G   11G  99% /