My script was doing speech recognition training fine, until recently I tried to scale up to train on more data, now it output this error.
ERROR: "backward.c", line 421: Failed to align audio to trancript: final state of the search is not reached
What does that mean? What can I do about it?
It looks like the model training proceed anyway, but not sure if this is an error I can ignore.
I checked out this link, but I am pretty sure my audio are sampled at 16KHz.
As explained in documentation:
Sometimes audio in your database doesn't match the transcription
properly. For example transcription file has the line “Hello world”
but in audio actually “Hello hello world” is pronounced. Training
process usually detects that and emits this message in the logs. If
there are too many such errors it most likely mean you misconfigured
something, for example you had a mismatch between audio and the text
caused by transcription reordering. Or input audio sample rate is wrong
If there are few errors, you can
ignore them. You might want to edit the transcription file to put
there exact word which were pronounced, in the case above you need to
edit the transcription file and put “Hello hello world” on
corresponding line. You might want to filter such prompts because they
affect acoustic model quality. In that case you need to enable forced
alignment stage in training.
Related
I need to detect the speech rate (speed of spoken words) an a audio file. Most of codes available including pyaudioanalysis etc provide sampling rate, silence detection, or even emotion detection.
The need is I want to know how fast speaker is speaking. Can anyone suggest some code or technique please.
I worked with speech to text but there are 2 main problems
Not all the words are correct that is produced by the engine.
There can be long pauses in between the text that doesn't help for the detection of speech rate.
I was working with PRAAT software, and there is an extension for this in python(https://github.com/YannickJadoul/Parselmouth). A detailed explanation of the procedure is given here
There is an option for detection of speech rate with the script(https://sites.google.com/site/speechrate/Home/praat-script-syllable-nuclei-v2). Using Parselmouth we can run the script. In case if you are ok with using PRAAT software here is a step by step analysis https://sites.google.com/site/speechrate/Home/tutorial.
The script returns no of syllables, no of pauses, duration, speech rate, articulation rate, ASD(speaking_time/no_of_syllables).
for reference paper-https://www.researchgate.net/publication/24274554_Praat_script_to_detect_syllable_nuclei_and_measure_speech_rate_automatically
check this https://github.com/Shahabks/myprosody, this could work even.
Hope this helps.
I need to be able to analyze (search thru) hundreds of WAV files and detect but not remove static noise. As done currently now, I must listen to each conversation and find the characteristic noise/static manually, which takes too much time. Ideally, I would need a program that can read each new WAV file and be able to detect characteristic signatures of the static noise such as periods of bursts of white noise or full audio band, high amplitude noise (like AM radio noise over phone conversation such as a wall of white noise) or bursts of peek high frequency high amplitude (as in crackling on the phone line) in a background of normal voice. I do not need to remove the noise but simply detect it and flag the recording for further troubleshooting. Ideas?
I can listen to the recordings and find the static or crackling but this takes time. I need an automated or batch process that can run on its own and flag the troubled call recordings (WAV files for a phone PBX). These are SIP and analog conversations depending on the leg of the conversation so RTSP/SIP packet analysis might be an option, but the raw WAV file is the simplest. I can use Audacity, but this still requires opening each file and looking at the visual representation of the audio spectrometry and is only a little faster than listening to each call but still cumbersome.
I currently have no code or methods for this task. I simply listen to each call wav file to find the noise.
I need a batch Wav file search that can render wav file recordings that contain the characteristic noise or static or crackling over the recording phone conversation.
Unless you can tell the program how the noise looks like, it's going to be challenging to run any sort of batch processing. I was facing a similar challenge and that prompted me to develop (free and open source) software to help user in audio exploration, analysis and signal separation:
App: https://audioexplorer.online/
Docs: https://tracek.github.io/audio-explorer/
Source code: https://github.com/tracek/audio-explorer
Essentially, it visualises audio as a 2d scatter plot rather than only "linear", as in waveform or spectrogram. When you upload audio the following happens:
Onsets are detected (based on high-frequency content algorithm from aubio) according to the threshold you set. Set it to None if you want all.
Per each audio fragment, calculate audio features based on your selection. There's no universal best set of features, all depends on the application. You might try for starter with e.g. Pitch statistics. Consider setting proper values for bandpass filter and sample length (that's the length of audio fragment we're going to use). Sample length could be in future established dynamically. Check docs for more info.
The result is that for each fragment you have many features, e.g. 6 or 60. That means we have then k-dimensional (where k is number of features) structure, which we then project to 2d space with dimensionality reduction algorithm of your selection. Uniform Manifold Approximation and Projection is a sound choice.
In theory, the resulting embedding should be such that similar sounds (according to features we have selected) are closely together, while different further apart. Your noise should be now separated from your "not noise" and form cluster.
When you hover over the graph, in right-upper corner a set of icons appears. One is lasso selection. Use it to mark points, inspect spectrogram and e.g. download table with features that describe that signal. At that moment you can also reduce the noise (extra button appears) in a similar way to Audacity - it analyses the spectrum and reduces these frequencies with some smoothing.
It does not completely solve your problem right now, but could severely cut the effort. Going through hundreds of wavs could take better part of the day, but you will be done. Want it automated? There's CLI (command-line interface) that I am developing at the same time. In not-too-distant future it should take what you have labelled as noise and signal and then use supervised machine learning to go through everything in batch mode.
Suggestions / feedback? Drop an issue on GitHub.
I'm currently working on creating a recipe for a script that would compare audio input with existing audio sources and return a match is any.
The idea is that the voice input would not be convertible to text. Those would be vocals such as dog ("woof") or cat ("meow") sound inputs.
In the end, I would like the script to conclude whether the input was a cat or dog sound, or none of the two.
I understand that It would require to pre process the sound input (low-pass; noise reduction etc), then do a spectrum analysis of the sound before comparing this to the existing spectrum analysis from the DB but I don't know where to start.
Are there any libraries for this kind of small project that could help?
How do I compare spectrum analysis?
How does spectrum analysis comparison take into account the possibility that two different people could make the same meow sound? Does it take into account a match up to a specific pourcentage?
Thanks for any guidance regarding this matter.
I am building a prototype of a sound detection app that will ultimately run on a phone (iPhone/Android). It needs to be near real-time to give fast enough response to the user when a particular sound is recognized. I am hoping to use tensorflow to actually build and train the model and then deploy it on mobile device.
What I am unsure about is best way to feed data to tensorflow for inference in this case.
Option 1: Feed only newly acquired samples to the model.
Here the model itself keeps a buffer of previous signal samples, to which new samples are appended and the whole thing get processed.
Something like:
samples = tf.placeholder(tf.int16, shape=(None))
buffer = tf.Variable([], trainable=False, validate_shape=False, dtype=tf.int16)
update_buffer = tf.assign(buffer, tf.concat(0, [buffer, samples]), validate_shape=False)
detection_op = ....process buffer...
session.run([update_buffer, detection_op], feed_dict={samples: [.....]})
This seems to work, but if the samples are pushed to the model 100 times a second, what's happening inside tf.assign (the buffer can grow big enough, and if tf.assign constantly allocates memory this may not work well)?
Option 2: Feed the whole recording to the model
Here the iPhone app keeps the state/recording samples, and feeds the whole recording to the model. The input can get quite large, and re-running the detection op on the whole recording will have to keep recomputing the same values each cycle.
Option 3: Feed a sliding window of data
Here the app keeps the data for the whole recording, but feeds only the latest slice of data to the model. E.g. last 2 sec at 2000 sampling rate == 4000 sample fed fed at the rate of 1/100 sec (each new 20 samples). The model may also need to keep some running totals for the whole recording.
Advise?
I'd need to know a bit more about your application requirements, but for simplicities sake I recommend starting with option #3. The usual way to approach this problem for arbitrary sounds is:
Have some trigger to detect the start of a sound or speech utterance. This can just be sustained audio levels, or something more advanced.
Run a spectrogram over a fixed size window, aligned with the start of the noise.
The rest of the network can just be a standard image detection one (usually cut down in size) to classify the sound.
There are a lot of variations and other possible approaches. For example for speech it's typical to use MFCC as your feature generator, and then run an LSTM to separate out phonemes, but since you mention sound detection I'm guessing you don't need anything this advanced.
On my system, using my USB microphone, I've found that the audio level that works best with CMU Sphinx is about 20% of the maximum. This gives me 75% voice recognition accuracy. If I amplify this digitally I get far worse recognition accuracy (25%). Why is this? What is the recommended audio level for Sphinx? [Also I am using 16,000 samples/sec, 16-bit.]
pocketsphinx decoder uses channel amplitude normalization. Initial normalization value is configured to 20% audio level indeed inside the model (-cmninit parameter in feat.params). However, the level is updated as you decode, so it has only effect on first utterance. If you properly decode in continuous mode, level should not matter. Do not restart recognizer for every utterance, let it adapt to the noise and audio level.