Dockerfile

FROM nvidia/cuda:12.9.0-runtime-ubuntu24.04

# Avoid interactive prompts during package installation
ENV DEBIAN_FRONTEND=noninteractive

# Install Python and system dependencies.
#
# NO ffmpeg, and NO torchcodec below: this pipeline only ever decodes .wav, which
# libsndfile1 (via soundfile/librosa) handles on its own. ffmpeg was here solely to satisfy
# torchcodec, which in turn was only needed to decode the dataset's Audio column — and the
# `datasets<4.0` pin below removes that need. Dropping ffmpeg saves ~630 MB of apt packages and
# most of this layer's build time (it was ~60 min of a recent Space build).
#
# THESE THREE GO TOGETHER — do not change one alone:
#   * unpin `datasets` (>=4.0) and its Audio column decodes via torchcodec, so torchcodec and
#     ffmpeg both have to come back;
#   * re-add torchcodec WITHOUT ffmpeg and `transformers.audio_utils.load_audio` gets worse, not
#     better: its default `backend="auto"` prefers torchcodec whenever it is importable, so it
#     would select a decoder whose FFmpeg shared libs are missing instead of falling back to
#     librosa. Stages 2 (transcribe) and 3 (SIM) both call it.
RUN apt-get update && apt-get install -y --no-install-recommends \
    python3 \
    python3-pip \
    python3-dev \
    git \
    libsndfile1 \
    && rm -rf /var/lib/apt/lists/*

# Set Python alias (Ubuntu 24.04 ships Python 3.12)
RUN ln -sf /usr/bin/python3 /usr/bin/python

# Allow pip to install packages system-wide in the container (PEP 668)
ENV PIP_BREAK_SYSTEM_PACKAGES=1

# Set working directory
WORKDIR /app

# Install PyTorch ecosystem (cu128 wheels for CUDA 12.8+/12.9 compat)
# torchvision is required by Qwen3-Omni's AutoProcessor, which wraps a video processor
# (Qwen2VLVideoProcessor) even when only text/audio inputs are used.
RUN pip install --no-cache-dir \
    torch==2.8.0 \
    torchaudio==2.8.0 \
    torchvision==0.23.0 \
    --index-url https://download.pytorch.org/whl/cu128

# Install common requirements (torch already installed above, pip will skip it)
# regex + kaldialign are required by the Open ASR Leaderboard `normalizer/` used for WER scoring.
# zhconv folds traditional Chinese to simplified for the CER scoring path (Seed-TTS zh splits).
# `datasets` is pinned <4.0 to match the other backend images (chatterbox, xtts, pocket-tts):
# 4.0 moved the Audio column's decoder to torchcodec, which pins exact torch minors and needs
# FFmpeg shared libs. 3.x decodes with soundfile/librosa instead, which is all this pipeline needs
# (.wav only) — and it keeps `prompt_audio["array"]`/`["sampling_rate"]` in run_eval.py reading a
# plain dict rather than relying on the AudioDecoder compat shim 4.x/5.x ship.
RUN pip install --no-cache-dir \
    git+https://github.com/huggingface/transformers.git \
    evaluate \
    "datasets<4.0" \
    librosa \
    jiwer \
    num2words \
    regex \
    kaldialign \
    zhconv \
    sentencepiece \
    protobuf \
    peft \
    g2p-en \
    soundfile

# Download NLTK data required by g2p-en (used by FastSpeech2ConformerTokenizer).
RUN python3 -c "import nltk; nltk.download('averaged_perceptron_tagger_eng'); nltk.download('cmudict')"

# Copy the full repository
COPY . /app

# Default entrypoint
ENTRYPOINT ["bash"]

# Keep-alive CMD so the Space runtime stays healthy. HF Jobs and `docker run`
# override this with their own command (e.g. `run_cohere.sh`).
EXPOSE 7860
CMD ["-c", "python3 -m http.server 7860"]