Due to the large number of audio samples on this page, all samples have been compressed (96 kb/s mp3). The uncompressed files are available for download at this repository. Audio clips which correspond to ground-truth data are generated by inverting ground-truth spectrograms. Samples shown here were selected based on diversity and quality. Samples used for quantitative experiments in the paper were randomly drawn.
Samples generated by the model conditioned on text and speaker ID. The conditioning text and speaker IDs are taken directly from the validation set (text in the dataset is unnormalized and unpunctuated).
Sample Speech Audio File Download Mp3
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For comparison, we train WaveNet on the same three unconditional audio generation tasks used to evaluate MelNet (single-speaker speech generation, multi-speaker speech generation, and music generation).
Like to transcribe a couple of long (Dutch) audio files. They are interviews which are about 60-120 minutes per file in length. Got only 8 files which I need to do manually, so not necessarily part of some automated software. Got some Azure credits, so thought to go with Azure Cognitive Services Speech to Text. Is there a sample somewhere for that?
So, I must find a solution to reduce speech samples to ca. 2kB per 0.7sek (one speech).
I am aware that it will be compromise between the length of WAV file and the possibility of understanding speech.
I have a 22 kHz mono audio recording, which is mainly speech, a reading. I would like to upsample somehow to 44 kHz, to improve the audible quality. I have read about there are AI methods for upsampling pictures, even videos to a higher resolution. Maybe there is some similar methods for audio too.
The recording comes from radio broadcast, so the recording was excellent studio quality, streamed in 128 kbps, 44 kHz, stereo, but somebody, who did the recording, downsampled it from the original 44 kHz broadcast to 22 kHz, mono. Hence the sound quality is far from optimal, sounds like an old telephone, I think, due to the missing high frequencies.
While I don't know about tools for upsampling using AI, I am challenging the assumption that the main problem with your sample is lost high frequncies due to resampling from 44kHz to 22kHz, so "just" upsampling is unlikely to solve your root problem.
The possibility of AI upsampling itself is sensible, though: While standard (mathematical) upsampling yields the answer to the question "which 44kHz signal sounds most like this given 22kHz signal?", AI-based upsampling is intended to answer the completely different question "which 44kHz real-world signal is most likely to sound like this when you (re)sample it at 22kHz". If the down-sampling is in fact the root of your problem, AI upsampling might very well fix it, as long as the AI is trained with signals of the right kind. E.g. if you have an AI trained for upsampling music, the AI might "assume" the speaker is actually singing all the time, and add some tonality that doesn't belong into your signal - so be careful on what AI methods use use.
On the other hand, I see two more likely causes than being sampled to 22kHz that might cause the "old telephone line" effect. Old telephone lines are way worse than 22kHz/16 bit sampling. 16kHz sample rate is already considered to be "HD telephony" nowadays. If the sound of the voice itself resembles old telephone lines, it has been treated considerably more badly than being filtered to a bandwidth of 10kHz (what is needed for resampling to 22kHz). An actual telephone line limits the frequencies to 0.3 to 3.4 kHz, and coarsely matches 8kHz sample rate.
The 22kHz file has been encoded in a lossy way (like MP3 at 64kbit/s or even lower). Especially for old MP3 encoders, it's very common to put a low-pass filter in fron of enoding to limit the amount of frequencies that might need encoding and thus reduce the amount of data. If lossy encoding is the root of the problem, you still might have success using AI to reconstruct a better sounding signal, but you need an AI that is trained to losses caused by low-bitrate MP3 encoding, not an AI that is trained to losses caused by resampling to 22kHz.
The file you have is monaureal. It does not convey any information about the room the speaker(s) is/are in. It sounds like the/all speaker(s) are directly in front of you. If there is not much "room sound" in it, you can "fix" the problem by using a stereo reverb process that puts the sample into an artificial room and provides different signals to the left and right ear just due to the fact that the artificial reflections from the left or right wall sound different. Furthermore, if there are multiple speakers, you might need to seperate them (put them at different locations in the virtual room). The easiest way would be to pan the sample to different stereo positions for different speakers. If multiple speakers are talking at the same time, separating them is a hard task. Even after panning the signal, adding some stereo reverb might help the sound.
Note that I am aware that this answer has to do more with psychoacoustics than with signal processing. I just want to provide a different angle to the issue. I am very much well aware that "adding noise" is usually not a solution but rather a problem.
how are you? I use DSEE-HX algorithms to try to fix your files, I hope it helped you. If you have smartphone samsung, please use UHQ in the audio settings, try to find some android internal audio recorder or use the cable to conect your smartphone to the computer and recording using audacity program.
This document is a guide to the basics of using Speech-to-Text.This conceptual guide covers the types of requests you can maketo Speech-to-Text, how to construct those requests, and how tohandle their responses. We recommend that all users of Speech-to-Textread this guide and one of the associated tutorials before diving intothe API itself.
Synchronous Recognition (REST and gRPC) sends audio data to the Speech-to-Text API,performs recognition on that data, and returns results after all audiohas been processed. Synchronous recognition requests are limited to audiodata of 1 minute or less in duration.
Asynchronous Recognition (REST and gRPC) sends audio data to the Speech-to-Text APIand initiates a Long Running Operation. Using this operation, you canperiodically poll for recognition results. Use asynchronous requests foraudio data of any duration up to 480 minutes.
Streaming Recognition (gRPC only) performs recognition on audio dataprovided within agRPC bi-directional stream.Streaming requests are designed for real-time recognition purposes, such ascapturing live audio from a microphone. Streaming recognition providesinterim results while audio is being captured, allowing result to appear,for example, while a user is still speaking.
Requests contain configuration parameters as well as audio data. The followingsections describe these type of recognition requests, the responses theygenerate, and how to handle those responses in more detail.
A Speech-to-Text API synchronous recognition request is the simplest method forperforming recognition on speech audio data. Speech-to-Text can process up to1 minute of speech audio data sent in a synchronous request. After Speech-to-Textprocesses and recognizes all of the audio, it returns a response.
A synchronous request is blocking, meaning that Speech-to-Text mustreturn a response before processing the next request. Speech-to-Texttypically processes audio faster than realtime, processing 30 seconds of audioin 15 seconds on average. In cases of poor audio quality, your recognitionrequest can take significantly longer.
You specify the sample rate of your audio in the sampleRateHertz fieldof the request configuration, and it must match the sample rate of the associated audiocontent or stream. Sample rates between 8000 Hz and 48000 Hz are supportedwithin Speech-to-Text. You can specify the sample rate for a FLAC orWAV file in the file header instead of using the sampleRateHertz field.A FLAC file must contain the sample rate in the FLAC header in order to besubmitted to the Speech-to-Text API.
If you have a choice when encoding the source material, capture audio using asample rate of 16000 Hz. Values lower than this may impair speech recognitionaccuracy, and higher levels have no appreciable effect on speech recognitionquality.
However, if your audio data has already been recorded at an existing samplerate other than 16000 Hz, do not resample your audio to 16000 Hz. Most legacytelephony audio, for example, use sample rates of 8000 Hz, which may give lessaccurate results. If you must use such audio, provide the audio to the SpeechAPI at its native sample rate.
Speech-to-Text's recognition engine supports a variety of languages anddialects. You specify the language (and national or regional dialect) of youraudio within the request configuration's languageCode field, using aBCP-47 identifier.
Speech-to-Text can include time offset values (timestamps)for the beginningand end of each spoken word that is recognized in the supplied audio. A timeoffset value represents the amount of time that has elapsed from the beginningof the audio, in increments of 100ms.
Time offsets are especially useful for analyzing longer audio files, where youmay need to search for a particular word in the recognized text andlocate it (seek) in the original audio. Time offsets are supported for allour recognition methods: recognize, streamingrecognize, andlongrunningrecognize.
To include time offsets in the results of your request, set theenableWordTimeOffsets parameter to true in your request configuration. Forexamples using the REST API or the Client Libraries, seeUsing Time Offsets (Timestamps).For example, you can include the enableWordTimeOffsets parameter in therequest configuration as shown here:
When you send an audio transcription request toSpeech-to-Text, you can improve the results that you receiveby specifying the source of the original audio. This allows theSpeech-to-Text API to process your audio files using amachine learning model trained to recognize speech audio from thatparticular type of source. 152ee80cbc
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