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Drop ffmpeg-python dependency and call ffmpeg directly. (#1242)

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* Drop ffmpeg-python dependency and call ffmpeg directly.

The last ffmpeg-python module release was in 2019[1], upstream seem to be
unavailable[2] and the project development seem to have stagnated[3].  As
the features it provide is trivial to replace using the Python native
subprocess module, drop the dependency.

 [1] <URL: https://github.com/kkroening/ffmpeg-python/tags >
 [2] <URL: https://github.com/kkroening/ffmpeg-python/issues/760 >
 [3] <URL: https://openhub.net/p/ffmpeg-python >

* Rewrote to use subprocess.run() instead of subprocess.Popen().

* formatting changes

* formatting update

* isort fix

* Error checking

* isort 🤦🏻

* flake8 fix

* minor spelling changes

---------

Co-authored-by: Jong Wook Kim <jongwook@openai.com>
This commit is contained in:
petterreinholdtsen 2023-05-04 19:53:59 +02:00 committed by GitHub
parent e69930cb9c
commit 8035e9ef48
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3 changed files with 20 additions and 13 deletions
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4
README.md
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@ -17,9 +17,7 @@ A Transformer sequence-to-sequence model is trained on various speech processing
## Setup
We used Python 3.9.9 and [PyTorch](https://pytorch.org/) 1.10.1 to train and test our models, but the codebase is expected to be compatible with Python 3.8-3.11 and recent PyTorch versions. The codebase also depends on a few Python packages, most notably [OpenAI's tiktoken](https://github.com/openai/tiktoken) for their fast tokenizer implementation and [ffmpeg-python](https://github.com/kkroening/ffmpeg-python) for reading audio files. You can download and install (or update to) the latest release of Whisper with the following command:
We used Python 3.9.9 and [PyTorch](https://pytorch.org/) 1.10.1 to train and test our models, but the codebase is expected to be compatible with Python 3.8-3.11 and recent PyTorch versions. The codebase also depends on a few Python packages, most notably [OpenAI's tiktoken](https://github.com/openai/tiktoken) for their fast tokenizer implementation. You can download and install (or update to) the latest release of Whisper with the following command:
pip install -U openai-whisper

1
requirements.txt
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@ -4,4 +4,3 @@ torch
tqdm
more-itertools
tiktoken==0.3.3
ffmpeg-python==0.2.0

28
whisper/audio.py
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@ -1,8 +1,8 @@
import os
from functools import lru_cache
from subprocess import CalledProcessError, run
from typing import Optional, Union
import ffmpeg
import numpy as np
import torch
import torch.nn.functional as F
@ -39,15 +39,25 @@ def load_audio(file: str, sr: int = SAMPLE_RATE):
-------
A NumPy array containing the audio waveform, in float32 dtype.
"""
# This launches a subprocess to decode audio while down-mixing
# and resampling as necessary. Requires the ffmpeg CLI in PATH.
# fmt: off
cmd = [
"ffmpeg",
"-nostdin",
"-threads", "0",
"-i", file,
"-f", "s16le",
"-ac", "1",
"-acodec", "pcm_s16le",
"-ar", str(sr),
"-"
]
# fmt: on
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(file, threads=0)
.output("-", format="s16le", acodec="pcm_s16le", ac=1, ar=sr)
.run(cmd=["ffmpeg", "-nostdin"], capture_stdout=True, capture_stderr=True)
)
except ffmpeg.Error as e:
out = run(cmd, capture_output=True, check=True).stdout
except CalledProcessError as e:
raise RuntimeError(f"Failed to load audio: {e.stderr.decode()}") from e
return np.frombuffer(out, np.int16).flatten().astype(np.float32) / 32768.0