I'm trying to pipe the output of ffmpeg to the codegen passing the input file directly to ffmpeg like:

decode = function (uri) {
      var self = this;

      // defaults
      var loglevel = self.logger.isDebug() ? 'debug' : 'warning';
      const args = [
        '-y',
        '-loglevel', loglevel,
        '-acodec', 'pcm_s16le',
        '-ar', 22050,
        '-ac', 1,
        '-f', 'wav',
        '-v', 'fatal',
        '-i', uri, // <--- I'm using local input instead of 'pipe:0
        'pipe:1'
      ];
      const opts = {
        stdio: ['pipe', 'pipe', process.stderr]
      };
      var decoder = cp.spawn('ffmpeg', args, opts);
      return decoder;
    }//decode

and then

// get decoder instance
var decoder = this.decode(mediaPath);
var fingerprinter = new Codegen();
// pipe fingerprint
decoder.stdout.pipe(fingerprinter);
// attach handlers
fingerprinter.on('end', resolve)
fingerprinter.on('message', msg => self.logger.info(msg))
fingerprinter.on('error', reject)
fingerprinter.on('close', resolve)
fingerprinter.on("data", function (data) {
    for (var i = 0; i < data.tcodes.length; i++) {
        console.log("time=" + data.tcodes[i] + " fingerprint=" + data.hcodes[i]);
      }
});

but I don't get any result here. What it seems to me is that I get the end handler, but not the data handler.

starting
type awesome_music.mp3 | node codegen_demo.js
gives me just one console output:
"fingerprints stream ended"

no other errors or logs appeared. awesome_music.mp3 is ~4s long.

please help

Ok, so I'm watching a html5 video and i would like to fingerprint the audio of the movie that you are watching.

is that possible with the AudioContext? My knowledge of AudioContext is close to nothing

I'm trying to understand why this function was chosen? Or could you break it down? Maybe I'm missing something obvious... Thanks!

f(freq(t1), freq(t2), dt) = freq(t1) + NFFT/2 * (freq(t2) + NFFT/2 * dt)

I got the following error when i run
curl http://radiofg.impek.com/fg | node node_modules/stream-audio-fingerprint/codegen_demo.js

  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed
  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0events.js:183
      throw er; // Unhandled 'error' event
      ^

Error: spawn ffmpeg ENOENT
    at Process.ChildProcess._handle.onexit (internal/child_process.js:190:19)
    at onErrorNT (internal/child_process.js:362:16)
    at _combinedTickCallback (internal/process/next_tick.js:139:11)
    at process._tickCallback (internal/process/next_tick.js:181:9)
    at Function.Module.runMain (module.js:696:11)
    at startup (bootstrap_node.js:204:16)
    at bootstrap_node.js:625:3
100  5808    0  5808    0     0   7902      0

Hello,
First of all thanks for such a nice tool.

I was curious if there is a threshold which can be tuned?
For example I have indexed 2 hours long audio file in json (I took an example from one of closed issues here) and cut out a 15 second clip from the same audio file which I am then comparing against this json file. It seems like some of the fingerprints match up throughout all the 2 hour audio file, but then repeats a lot of times exactly where it should. ( I have tuned the code a little bit so timecode is put as exact timestamp)

So for example if I compare the small 15 second clip against the whole 2 hours of audio, I get this -

{ time: '0:40', fingerprint: 920332 },
{ time: '0:56', fingerprint: 207915 },
{ time: '1:53', fingerprint: 2826250 },
{ time: '2:10', fingerprint: 4787722 },
{ time: '2:56', fingerprint: 2826250 },
{ time: '6:34', fingerprint: 4157 },
{ time: '6:34', fingerprint: 15632 },
{ time: '12:07', fingerprint: 14114 },
{ time: '12:07', fingerprint: 8759 },
{ time: '13:07', fingerprint: 47075 },
{ time: '13:07', fingerprint: 58295 },
{ time: '15:51', fingerprint: 1706252 },
{ time: '25:35', fingerprint: 920332 },
{ time: '26:03', fingerprint: 3934509 },
{ time: '26:04', fingerprint: 8759 },
{ time: '26:04', fingerprint: 14114 },
{ time: '27:48', fingerprint: 92813 },
{ time: '30:40', fingerprint: 599052 },
{ time: '32:24', fingerprint: 2439691 },
{ time: '33:51', fingerprint: 68644 },
{ time: '35:03', fingerprint: 211492 },
{ time: '36:32', fingerprint: 921098 },
{ time: '39:33', fingerprint: 8759 },
{ time: '39:33', fingerprint: 14114 },
{ time: '40:36', fingerprint: 1706252 },
{ time: '44:24', fingerprint: 4787722 },
{ time: '45:24', fingerprint: 2949928 },
{ time: '46:37', fingerprint: 1901062 },
{ time: '48:08', fingerprint: 4457230 },
{ time: '48:33', fingerprint: 1904898 },
{ time: '48:36', fingerprint: 197136 },
{ time: '49:23', fingerprint: 68644 },
{ time: '49:57', fingerprint: 4260376 },
{ time: '50:21', fingerprint: 270338 },
{ time: '50:28', fingerprint: 1185794 },
{ time: '50:28', fingerprint: 4260376 },
{ time: '59:07', fingerprint: 145932 },
{ time: '59:54', fingerprint: 1706252 },
{ time: '1:02:23', fingerprint: 599052 },
{ time: '1:05:09', fingerprint: 985892 },
{ time: '1:08:26', fingerprint: 2439691 },
{ time: '1:09:03', fingerprint: 42892 },
{ time: '1:09:03', fingerprint: 36007 },
{ time: '1:09:49', fingerprint: 145932 },
{ time: '1:12:17', fingerprint: 1704723 },
{ time: '1:12:24', fingerprint: 374162 },
{ time: '1:12:33', fingerprint: 393732 },
{ time: '1:12:55', fingerprint: 131587 },
---- HERE STARTING THE 15 sec clip -----
{ time: '1:13:00', fingerprint: 197152 },
{ time: '1:13:00', fingerprint: 278816 },
{ time: '1:13:00', fingerprint: 60099 },
{ time: '1:13:00', fingerprint: 50154 },
{ time: '1:13:00', fingerprint: 615489 },
{ time: '1:13:00', fingerprint: 736100 },
{ time: '1:13:01', fingerprint: 1114683 },
{ time: '1:13:01', fingerprint: 2687520 },
{ time: '1:13:01', fingerprint: 666951 },
{ time: '1:13:01', fingerprint: 3672578 },
{ time: '1:13:01', fingerprint: 4786747 },
{ time: '1:13:01', fingerprint: 921098 },
{ time: '1:13:02', fingerprint: 4100733 },
{ time: '1:13:03', fingerprint: 5243420 },
{ time: '1:13:04', fingerprint: 1250050 },
{ time: '1:13:04', fingerprint: 605971 },
{ time: '1:13:04', fingerprint: 1835539 },
{ time: '1:13:04', fingerprint: 2900031 },
{ time: '1:13:04', fingerprint: 3489811 },
{ time: '1:13:04', fingerprint: 266560 },
{ time: '1:13:04', fingerprint: 1904898 },
{ time: '1:13:04', fingerprint: 730129 },
{ time: '1:13:04', fingerprint: 992320 },
{ time: '1:13:04', fingerprint: 2630658 },
{ time: '1:13:05', fingerprint: 5139008 },
{ time: '1:13:05', fingerprint: 1974808 },
{ time: '1:13:06', fingerprint: 2838562 },
{ time: '1:13:06', fingerprint: 3774058 },
{ time: '1:13:06', fingerprint: 993872 },
{ time: '1:13:06', fingerprint: 3811874 },
{ time: '1:13:06', fingerprint: 2168618 },
{ time: '1:13:06', fingerprint: 3151696 },
{ time: '1:13:06', fingerprint: 1051671 },
{ time: '1:13:06', fingerprint: 3214378 },
{ time: '1:13:07', fingerprint: 590348 },
{ time: '1:13:07', fingerprint: 1638935 },
{ time: '1:13:07', fingerprint: 2556461 },
{ time: '1:13:08', fingerprint: 1580627 },
{ time: '1:13:08', fingerprint: 472094 },
{ time: '1:13:08', fingerprint: 2045011 },
{ time: '1:13:08', fingerprint: 1134644 },
{ time: '1:13:08', fingerprint: 1593374 },
{ time: '1:13:08', fingerprint: 268112 },
{ time: '1:13:08', fingerprint: 1382196 },
{ time: '1:13:08', fingerprint: 1840926 },
{ time: '1:13:10', fingerprint: 928576 },
{ time: '1:13:10', fingerprint: 1746845 },
{ time: '1:13:10', fingerprint: 2032171 },
{ time: '1:13:10', fingerprint: 1874599 },
{ time: '1:13:10', fingerprint: 226970 },
{ time: '1:13:10', fingerprint: 1144401 },
{ time: '1:13:11', fingerprint: 69146 },
{ time: '1:13:11', fingerprint: 2172969 },
{ time: '1:13:12', fingerprint: 3023912 },
{ time: '1:13:12', fingerprint: 211492 },
{ time: '1:13:12', fingerprint: 1575226 },
{ time: '1:13:12', fingerprint: 669961 },
{ time: '1:13:12', fingerprint: 1114681 },
{ time: '1:13:13', fingerprint: 75345 },
{ time: '1:13:13', fingerprint: 393766 },
{ time: '1:13:13', fingerprint: 2430722 },
{ time: '1:13:14', fingerprint: 2818583 },
---- HERE ENDING THE 15 sec clip -----
{ time: '1:14:45', fingerprint: 328196 },
{ time: '1:14:53', fingerprint: 268290 },
{ time: '1:15:14', fingerprint: 1901062 },
{ time: '1:16:43', fingerprint: 2630658 },
{ time: '1:18:06', fingerprint: 68644 },
{ time: '1:20:19', fingerprint: 3083304 },
{ time: '1:21:07', fingerprint: 204804 },
{ time: '1:21:07', fingerprint: 135200 },
{ time: '1:26:13', fingerprint: 4787722 },
{ time: '1:27:25', fingerprint: 96683 },
{ time: '1:27:41', fingerprint: 135200 },
{ time: '1:30:44', fingerprint: 1908750 },
{ time: '1:36:48', fingerprint: 204802 },
{ time: '1:40:22', fingerprint: 183452 },
{ time: '1:42:27', fingerprint: 938380 },
{ time: '1:45:04', fingerprint: 1517582 },
{ time: '1:55:11', fingerprint: 210434 },
{ time: '1:55:55', fingerprint: 918591 },
---- HERE STARTING THE 15 sec clip again which is expected -----
{ time: '1:56:21', fingerprint: 393732 },
{ time: '1:56:22', fingerprint: 2884100 },
{ time: '1:56:22', fingerprint: 666951 },
{ time: '1:56:22', fingerprint: 2501890 },
{ time: '1:56:22', fingerprint: 1408327 },
{ time: '1:56:22', fingerprint: 1182253 },
{ time: '1:56:22', fingerprint: 3672578 },
{ time: '1:56:23', fingerprint: 2826250 },
{ time: '1:56:23', fingerprint: 3899517 },
{ time: '1:56:23', fingerprint: 5275719 },
{ time: '1:56:23', fingerprint: 1969696 },
{ time: '1:56:23', fingerprint: 4787722 },
{ time: '1:56:25', fingerprint: 70439 },
{ time: '1:56:25', fingerprint: 1704723 },
{ time: '1:56:25', fingerprint: 1770279 },
{ time: '1:56:26', fingerprint: 2100994 },
{ time: '1:56:27', fingerprint: 2168618 },
{ time: '1:56:28', fingerprint: 1638935 },
{ time: '1:56:28', fingerprint: 2556461 },
{ time: '1:56:29', fingerprint: 4079149 },
{ time: '1:56:31', fingerprint: 1901059 },
{ time: '1:56:31', fingerprint: 938380 },
{ time: '1:56:32', fingerprint: 69146 },
{ time: '1:56:33', fingerprint: 131642 },
{ time: '1:56:33', fingerprint: 270338 },
{ time: '1:56:33', fingerprint: 401466 },
{ time: '1:56:35', fingerprint: 2818583 },
{ time: '1:56:35', fingerprint: 4935959 },
---- HERE ENDING THE 15 sec clip again which is expected -----
{ time: '1:56:48', fingerprint: 134147 },
{ time: '1:59:00', fingerprint: 131587 },
{ time: '1:59:01', fingerprint: 197136 },
{ time: '1:59:30', fingerprint: 657666 },
{ time: '1:59:42', fingerprint: 268290 },
{ time: '1:59:48', fingerprint: 4787722 },
{ time: '2:00:19', fingerprint: 1185828 },
{ time: '2:01:43', fingerprint: 920332 },
{ time: '2:02:39', fingerprint: 3214378 },
{ time: '2:05:22', fingerprint: 2826250 },
{ time: '2:06:55', fingerprint: 4157 },
{ time: '2:06:55', fingerprint: 15632 },
{ time: '2:07:38', fingerprint: 68644 },
{ time: '2:08:48', fingerprint: 1775628 }
]

So as you can see there is a lot of "noise" but I expect thats because the audio is containing voice which might be similar troughout the audio fingerprintwise. Or maybe I am doing something completely wrong?

Hi @dest4 ,

If I'm not mistaken, you use 12-second windows to generate fingerprints, thus it is not possible to use this to match parts of audio that are shorter than 12 seconds and that are spaced by less than 12 seconds from a longer stream. Is this correct? Would it be possible to do it my by changing some of the constants and which (e.d. to make it work with 3 or 4 seconds)?

To give a bit of context - I have a longer audio recording (a voice over recording session - several hours), from which a selection of takes have already been cut out into separate files and slightly processed (so volume and quality is not the same). I need to find at which positions (in seconds) did each of the files originate. As it is right now, for some of them it doesn't find a single match - presumably because they are too close together when in the large file.

Thanks!

Hi, this module is awesome! I am brand new to audio fingerprint, this module get me a good opportunity to learn how to code.

But I found that I got more than one fingerprint for each offset time, is it normal?

Sample Output:

time=34 fingerprint=238707
time=34 fingerprint=216179
time=34 fingerprint=347159
time=34 fingerprint=478295
time=43 fingerprint=601676
time=43 fingerprint=929303
time=43 fingerprint=1060439
time=45 fingerprint=132398
time=45 fingerprint=1049879
time=45 fingerprint=2360577

I am confused how to use these hashes to search in the database for matching hashes.

For example, if I have a record in database time=43 fingerprint=601676, does it mean the sample hit this db record?
or it requires a full matching time=43 fingerprint=601676, time=43 fingerprint=929303, time=43 fingerprint=1060439?

I would like to keep all the hcodes array hashes of the same size for every audio sample. Now assumed that we consider the same file chunk size for each audio sample, would be that possibile?

I encountered an error while running my Node.js application using version 18.14.0. The application throws the following error message:

Error opening input files: Invalid data found when processing input
node:events:491
      throw er; // Unhandled 'error' event
      ^

Error: write EPIPE
    at afterWriteDispatched (node:internal/stream_base_commons:160:15)
    at writevGeneric (node:internal/stream_base_commons:143:3)
    at Socket._writeGeneric (node:net:928:11)
    at Socket._writev (node:net:937:8)
    at doWrite (node:internal/streams/writable:409:12)
    at clearBuffer (node:internal/streams/writable:564:5)
    at onwrite (node:internal/streams/writable:464:7)
    at WriteWrap.onWriteComplete [as oncomplete] (node:internal/stream_base_commons:106:10)

Emitted 'error' event on Socket instance at:
    at Socket.onerror (node:internal/streams/readable:785:14)
    at Socket.emit (node:events:513:28)
    at emitErrorNT (node:internal/streams/destroy:151:8)
    at emitErrorCloseNT (node:internal/streams/destroy:116:3)
    at process.processTicksAndRejections (node:internal/process/task_queues:82:21) {
  errno: -4047,
  code: 'EPIPE',
  syscall: 'write'
}

Node.js version: v18.14.0

in the demo js, I believe it processes the wav header as well as the data :(.
the ffmpeg options are:
'-i', 'pipe:0',
'-acodec', 'pcm_s16le',
'-ar', 22050,
'-ac', 1,
'-f', 'wav',
'-v', 'fatal',
'pipe:1'

these are better, providing only the raw samples:
'-i', 'pipe:0',
'-map', '0:a',
'-acodec', 'pcm_s16le',
'-ar', 22050,
'-ac', 1,
'-f', 'data',
'-v', 'fatal',
'pipe:1'

The fingerprints produced are different.
It is quite odd that the fingerprints are SO different just for a few added bytes at the start.... quite a few common fingerprints, but >75% are different, and not just offset by time.

Hi! You don't know me, I don't;t I know you, but I couldn't believe my eyes when randomly skimming reddit I saw your post. For reasons soon to be apparent it and it filled me with joy and happy memories)

Well more than a decade ago (I think 2001), me and some wet behind the ears friends had this crazy idea that had never occurred to anyone before -- through the constructor of certain transfer functions, we could APPROXIMATE a metric space of sound, where distance corresponded to "how similar are these two sounds" This metric, when combined with more classical ML tools like PCA then SVM then Q trees for search and neighborest matching (thoughthat was slow) allowed us to so what felt at the time completely unheard of. We could actually take a random CD from an investors car, play any point on it, and within seconds know exactly the song and location.

But what I was always more curious about is the function AXA not AXB. THe function showing varying levels of self similarity within the same clip i just KNEW would eventually be worth it and at the least, it's really cool!

So, in the final death throws of the company, I put together a quick demo that would take as input audio stations and give as output exact timestmps of every time every single commercial was played. I thouhght it was cool, but had no idea of anyone would want such a thing.

Anyway, I'm nto sure where I was going but thanks for a pleasant walk down memory land, =)

This might be a dumb question, if so I am sorry.

I looked through the code and the example and am now properly confused.

What I'm doing to test (albeit a bit awkwardly) is piping my microphone input using ffmpeg, to an example file with node, but I notice that the fingerprints very rarely match those that I generated based on the source file.

The way I thought it worked was:

1. Create fingerprints for a source file (I used the example in the readme)
2. Create fingerprints for the input (for example microphone) and match against against previously generated fingerprints.

Like I said, I rarely have any results. Do I need to make it more sensitive? If so, how would I do that? Or am I not supposed to use my microphone as input with this lib?

Thanks!

Update:

If it helps, here's the code I'm using (a mess because it's just me going nuts):

var decoder = require('child_process').spawn('ffmpeg', [
	'-i', 'pipe:0',
	'-acodec', 'pcm_s16le',
	'-ar', 22050,
	'-ac', 1,
	'-f', 'wav',
	'-v', 'fatal',
	'pipe:1'
], { stdio: ['pipe', 'pipe', process.stderr] });

process.stdin.pipe(decoder.stdin);

var Codegen = require("stream-audio-fingerprint");
var fingerprinter = new Codegen();
decoder.stdout.pipe(fingerprinter);

const buildDB = process.argv[2] === 'db';
const live = process.argv[2] === 'live';
const database = [];
const db = buildDB ? null : require('./db.json');

fingerprinter.on("data", function(data) {
  const suspects = [];

  for (var i=0; i < data.tcodes.length; i++) {
    if (buildDB) {
      database.push({ time: data.tcodes[i], fingerprint: data.hcodes[i] })
    } else if (live) {
      suspects.push(data.hcodes[i]);
    } else {
      database.push(data.hcodes[i]);
    }
  }

  if (live) {
    const res = db.filter(x => suspects.indexOf(x.fingerprint) > -1);

    console.log({ tada: res });
  }
}).on('end', () => {
  if (buildDB) {
    console.log(JSON.stringify(database));
  } else {
    const db = require('./db.json');
    const results = [];

    const res = db.filter(x => database.indexOf(x.fingerprint) > -1);

    console.log({ tada: res });
  }
});

The way I run it is:

1. Store above file as index.js in a directory.
2. Make sure there's a source .wav file in the same directory. Let's call it my_file.wav
3. Make sure you have this file on an external device for playing. I have it on my phone (iPhone 11 pro)
4. "Index" the audio file: cat my_file.wav | node index.js db > db.json
5. Pipe your mic into the file: ffmpeg -f avfoundation -i ":0" -ar 22050 -acodec pcm_s16le -v fatal -f wav pipe:1 | node index.js live
6. Start playing the source audio on the external device (again, I'm playing it on my phone).
7. Observe some matches, very rarely (example: { tada: [ { time: 19433, fingerprint: 139300 } ] })

Sorry to bother but any lead on how to match 2 fingerprints ?

Say I have generated some fingerprints and saved to a database, and now have a radio stream and want to match againts that database. Any lead on this ?

Thanks a lot for your work.

Is there had chance to do this fingerprinting with ExpressJS or MQ so its run as standalone instead of executing cat music.mp3 | node src.js ? Just wonder how since this works only with stdin pipe

Oh! also, any chance to get more fingerprint (way more accurate)? Is this just play with the overlap?

1. What is the fingerprint size of S.A.F. vs Chromaprint?
2. How long does it take to discern a match in S.A.F. vs Chromaprint?
3. How accurate is S.A.F. vs Chromaprint given the same files?
4. Does S.A.F. and Chromaprint work even if there are white noise?

See: https://github.com/acoustid/chromaprint

hi
thanks for publish this library
run in window command prompt with this command
type two.mp3 | node codegen_demo.js

but not creat out-fft.png OR out-thr.png OR out-raw.png
please help me
thanks

1. According to https://en.wikipedia.org/wiki/Just-noticeable_difference humans can only notice tones of 10 cents (1/10 of a semitone) or more, so is it possible to reduce tone differences to an integer to the nearest 10cents instead of a float?
2. According to https://www.reddit.com/r/askscience/comments/5dpu0z/what_is_the_fastest_beats_per_minute_we_can_hear/ humans can only notice beats of 1500-1800BPM or 25-30BPS or slower, so is it possible to reduce the timed difference to an integer to the nearest 40ms (or 50ms, or 20ms/25ms for higher accuracy) instead of a float?
3. According to https://en.wikipedia.org/wiki/List_of_chords most chords have less than 8 notes, so would adding f3~f6 make everything more accurate?

P.S. It would be great to have an awesome-audio-fingerprint that shows a list of projects that uses this repo

Description:
I encountered an error while running my Node.js application using version 18.14.0. The application throws the following error message:

`
Error opening input files: Invalid data found when processing input
node:events:491
throw er; // Unhandled 'error' event
^

Error: write EPIPE
at afterWriteDispatched (node:internal/stream_base_commons:160:15)
at writevGeneric (node:internal/stream_base_commons:143:3)
at Socket._writeGeneric (node:net:928:11)
at Socket._writev (node:net:937:8)
at doWrite (node:internal/streams/writable:409:12)
at clearBuffer (node:internal/streams/writable:564:5)
at onwrite (node:internal/streams/writable:464:7)
at WriteWrap.onWriteComplete [as oncomplete] (node:internal/stream_base_commons:106:10)

Emitted 'error' event on Socket instance at:
at Socket.onerror (node:internal/streams/readable:785:14)
at Socket.emit (node:events:513:28)
at emitErrorNT (node:internal/streams/destroy:151:8)
at emitErrorCloseNT (node:internal/streams/destroy:116:3)
at process.processTicksAndRejections (node:internal/process/task_queues:82:21) {
errno: -4047,
code: 'EPIPE',
syscall: 'write'
}

Node.js version: v18.14.0
`

Hi i want to convert tcodes to seconds, is there any way to convert the tcodes to the actual seconds on the music? (i have the original song fingerprint on the db, the hashes shown on the image is just a part of the original one)
Also, is the hash generator sensitive about distortion, noise and the other audio glitch? (i hope not)

Hello, I have tried to do some plotting turning DO_PLOT=true.
It works, but the plots seems to be stretched in some way


Any idea?

Thanks again for your help!

Hi,
I'm interested in finding near-duplicate audio files. My dataset is about 3000 thousands short audio files, between 0.5 seconds to 5 seconds. Unlike Shazam, both the "target" audio (i.e. the songs in Shazam's case) and the user input are short, and both might contain noise.

Can this library help?
If so, are there any recommendations for tuning parameters?

N.B - if a file is matched to multiple other files, it's fine - I have a less efficient algorithm that can verify which match is correct. In other words, I can handle some amount of false positives, but I don't want false negatives.

Any chance to stop the fingerprinting process instead leave it untill it emit 'end' event?
Figure1.1: Audio recognized as A Music
Figure1.2: End fingerprinting process because the Music already fingerprinted
Thank you, Anyway Merry Christmas! 😃

Louisde-MBP:stream-audio-fingerprint louis$ sudo curl http://radiofg.impek.com/fg | nodejs codegen_demo.js
bash: nodejs: command not found
% Total % Received % Xferd Average Speed Time Time Time Current
Dload Upload Total Spent Left Speed
100 17376 0 17376 0 0 6608 0 --:--:-- 0:00:02 --:--:-- 6609
curl: (23) Failed writing body (0 != 1448)

I figured maybe just my problem so I also tried this below:

Louisde-MBP:stream-audio-fingerprint louis$ curl http://radiofg.impek.com/fg | node codegen_demo.js
% Total % Received % Xferd Average Speed Time Time Time Current
Dload Upload Total Spent Left Speed
0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0events.js:183
throw er; // Unhandled 'error' event
^
Error: spawn ffmpeg ENOENT
at _errnoException (util.js:1024:11)
at Process.ChildProcess._handle.onexit (internal/child_process.js:190:19)
at onErrorNT (internal/child_process.js:372:16)
at _combinedTickCallback (internal/process/next_tick.js:138:11)
at process._tickCallback (internal/process/next_tick.js:180:9)
at Function.Module.runMain (module.js:678:11)
at startup (bootstrap_node.js:187:16)
at bootstrap_node.js:608:3
100 23168 0 23168 0 0 11617 0 --:--:-- 0:00:01 --:--:-- 11613
curl: (23) Failed writing body (0 != 8688)

Still failed