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Submit bugs and patches
13 avril 2011Unfortunately a software is never perfect.
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Sur d’autres sites (9109)
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Error audio loading when runing Whisper Open AI model
9 juin 2024, par John mickThe problem I'm trying to solve is that I can't run Whisper model for some audio, it says something related to audio decoding.


payload.wav: Invalid data found when processing input.
raise RuntimeError(f"Failed to load audio: {e.stderr.decode()}") from e


I tried using the
micro-machines.wavand it works fine but when i used other audio it gives me an error.

import whisper

model = whisper.load_model("base")
text=model.transcribe('micro-machines.wav',fp16=False)
print(text)
text=model.transcribe('payload.wav',fp16=False)
print(text)


Error I'm getting for payload :


d:\...\venv\lib\site-packages\whisper\transcribe.py:79: UserWarning: FP16 is not supported on CPU; using FP32 instead
 warnings.warn("FP16 is not supported on CPU; using FP32 instead") 
Traceback (most recent call last):
 File "d:\...\venv\lib\site-packages\whisper\audio.py", line 42, in load_audio
 ffmpeg.input(file, threads=0) 
 File "d:\...\venv\lib\site-packages\ffmpeg\_run.py", line 325, in run 
 raise Error('ffmpeg', out, err) 
ffmpeg._run.Error: ffmpeg error (see stderr output for detail) 

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
 File "C:\....\Python\Python39\lib\runpy.py", line 197, in _run_module_as_main
 return _run_code(code, main_globals, None,
 File "C:\.....\Python\Python39\lib\runpy.py", line 87, in _run_code
 exec(code, run_globals)
 File "D:\...\venv\Scripts\whisper.exe\__main__.py", line 7, in <module>
 File "d:\...\venv\lib\site-packages\whisper\transcribe.py", line 314, in cli
 result = transcribe(model, audio_path, temperature=temperature, **args)
 File "d:\...\venv\lib\site-packages\whisper\transcribe.py", line 85, in transcribe
 mel = log_mel_spectrogram(audio)
 File "d:\...\venv\lib\site-packages\whisper\audio.py", line 111, in log_mel_spectrogram
 audio = load_audio(audio)
 File "d:\...\venv\lib\site-packages\whisper\audio.py", line 47, in load_audio
 raise RuntimeError(f"Failed to load audio: {e.stderr.decode()}") from e
RuntimeError: Failed to load audio: ffmpeg version 6.0-essentials_build-www.gyan.dev Copyright (c) 2000-2023 the FFmpeg developers
 built with gcc 12.2.0 (Rev10, Built by MSYS2 project)
 configuration: --enable-gpl --enable-version3 --enable-static --disable-w32threads --disable-autodetect --enable-fontconfig --enable-iconv --enable-gnutls --enable-libxml2 --enab
le-gmp --enable-lzma --enable-zlib --enable-libsrt --enable-libssh --enable-libzmq --enable-avisynth --enable-sdl2 --enable-libwebp --enable-libx264 --enable-libx265 --enable-libxv
id --enable-libaom --enable-libopenjpeg --enable-libvpx --enable-libass --enable-libfreetype --enable-libfribidi --enable-libvidstab --enable-libvmaf --enable-libzimg --enable-amf 
--enable-cuda-llvm --enable-cuvid --enable-ffnvcodec --enable-nvdec --enable-nvenc --enable-d3d11va --enable-dxva2 --enable-libmfx --enable-libgme --enable-libopenmpt --enable-libo
pencore-amrwb --enable-libmp3lame --enable-libtheora --enable-libvo-amrwbenc --enable-libgsm --enable-libopencore-amrnb --enable-libopus --enable-libspeex --enable-libvorbis --enab
le-librubberband
 libavutil 58. 2.100 / 58. 2.100
 libavcodec 60. 3.100 / 60. 3.100
 libavformat 60. 3.100 / 60. 3.100
 libavdevice 60. 1.100 / 60. 1.100
 libavfilter 9. 3.100 / 9. 3.100
 libswscale 7. 1.100 / 7. 1.100
 libswresample 4. 10.100 / 4. 10.100
 libpostproc 57. 1.100 / 57. 1.100
payload.wav: Invalid data found when processing input
</module>

I tried searching for solutions and I found one which says It appears that the code failed to load the audio file for some reason and even failed to display that error because e.stderr did not contain a valid UTF-8 string


-
ffmpeg and libaom compilation failed "unable to open include file `third_party/x86inc/x86inc.asm"
7 octobre 2023, par samI'm trying to build ffmpeg using this guide :
https://trac.ffmpeg.org/wiki/CompilationGuide/Ubuntu


the problem is when i try to compile libaom using the following commands :


cd ~/ffmpeg_sources && \
git -C aom pull 2> /dev/null || git clone --depth 1 https://aomedia.googlesource.com/aom && \
mkdir -p aom_build && \
cd aom_build && \
PATH="$HOME/bin:$PATH" cmake -G "Unix Makefiles" -DCMAKE_INSTALL_PREFIX="$HOME/ffmpeg_build" -DENABLE_TESTS=OFF -DENABLE_NASM=on ../aom && \
PATH="$HOME/bin:$PATH" make && \
make install


i get the following error :


/root/ffmpeg_sources/aom/aom_dsp/x86/sad4d_sse2.asm:14: fatal: unable to open include file `third_party/x86inc/x86inc.asm'
CMakeFiles/aom_dsp_encoder_sse2.dir/build.make:62: recipe for target 'CMakeFiles/aom_dsp_encoder_sse2.dir/aom_dsp/x86/sad4d_sse2.asm.o' failed
make[2]: *** [CMakeFiles/aom_dsp_encoder_sse2.dir/aom_dsp/x86/sad4d_sse2.asm.o] Error 1
CMakeFiles/Makefile2:842: recipe for target 'CMakeFiles/aom_dsp_encoder_sse2.dir/all' failed
make[1]: *** [CMakeFiles/aom_dsp_encoder_sse2.dir/all] Error 2
Makefile:129: recipe for target 'all' failed
make: *** [all] Error 2


Is there any fix for this issue ?


-
React Native Expo File System : open failed : ENOENT (No such file or directory)
9 février 2023, par coloradayI'm getting this error in a bare React Native project :


Possible Unhandled Promise Rejection (id: 123):
Error: /data/user/0/com.filsufius.VisionishAItest/files/image-new-♥d.jpg: open failed: ENOENT (No such file or directory)


The same code was saving to File System with no problem yesterday, but today as you can see I am getting an ENOENT error, plus I am getting these funny heart shapes ♥d in the path. Any pointers as to what might be causing this, please ? I use npx expo run:android to builld app locally and expo start —dev-client to run on a physical Android device connected through USB.


import { Image, View, Text, StyleSheet } from "react-native";
import * as FileSystem from "expo-file-system";
import RNFFmpeg from "react-native-ffmpeg";
import * as tf from "@tensorflow/tfjs";
import * as cocossd from "@tensorflow-models/coco-ssd";
import { decodeJpeg, bundleResourceIO } from "@tensorflow/tfjs-react-native";

const Record = () => {
 const [frames, setFrames] = useState([]);
 const [currentFrame, setCurrentFrame] = useState(0);
 const [model, setModel] = useState(null);
 const [detections, setDetections] = useState([]);

 useEffect(() => {
 const fileName = "image-new-%03d.jpg";
 const outputPath = FileSystem.documentDirectory + fileName;
 RNFFmpeg.execute(
 "-y -i https://res.cloudinary.com/dannykeane/video/upload/sp_full_hd/q_80:qmax_90,ac_none/v1/dk-memoji-dark.m3u8 -vf fps=25 -f mjpeg " +
 outputPath
 )
 .then((result) => {
 console.log("Extraction succeeded:", result);
 FileSystem.readDirectoryAsync(FileSystem.documentDirectory).then(
 (files) => {
 setFrames(
 files
 .filter((file) => file.endsWith(".jpg"))
 .sort((a, b) => {
 const aNum = parseInt(a.split("-")[2].split(".")[0]);
 const bNum = parseInt(b.split("-")[2].split(".")[0]);
 return aNum - bNum;
 })
 );
 }
 );
 })
 .catch((error) => {
 console.error("Extraction failed:", error);
 });
 }, []);

 useEffect(() => {
 tf.ready().then(() => cocossd.load().then((model) => setModel(model)));
 }, []);
 useEffect(() => {
 if (frames.length && model) {
 const intervalId = setInterval(async () => {
 setCurrentFrame((currentFrame) =>
 currentFrame === frames.length - 1 ? 0 : currentFrame + 1
 );
 const path = FileSystem.documentDirectory + frames[currentFrame];
 const imageAssetPath = await FileSystem.readAsStringAsync(path, {
 encoding: FileSystem.EncodingType.Base64,
 });
 const imgBuffer = tf.util.encodeString(imageAssetPath, "base64").buffer;
 const imageData = new Uint8Array(imgBuffer);
 const imageTensor = decodeJpeg(imageData, 3);
 console.log("after decodeJpeg.");
 const detections = await model.detect(imageTensor);
 console.log(detections);
 setDetections(detections);
 }, 100);
 return () => clearInterval(intervalId);
 }
 }, [frames, model]);

 
 return (
 <view style="{styles.container}">
 
 <view style="{styles.predictions}">
 {detections.map((p, i) => (
 <text key="{i}" style="{styles.text}">
 {p.class}: {(p.score * 100).toFixed(2)}%
 </text>
 ))}
 </view>
 </view>
 );
};

const styles = StyleSheet.create({
 container: {
 flex: 1,
 alignItems: "center",
 justifyContent: "center",
 },
 image: {
 width: 300,
 height: 300,
 resizeMode: "contain",
 },
 predictions: {
 width: 300,
 height: 100,
 marginTop: 20,
 },
 text: {
 fontSize: 14,
 textAlign: "center",
 },
});

export default Record;```


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