From Newsgroup: comp.mobile.android

Alan Peeling wrote:

On 30/04/2026 12:39, Maria Sophia wrote:

Here are some screenshots in series using both wired and wireless scrcpy.

Thanks for your very comprehensive post. My experience so far has been
that SCRCPY on USB works some of the time And SCRCPY on Wi-Fi won't
connect at all. As Wi-Fi connection seems to be a lot more faff than USB connection I think I'll forget about it.

I don't disagree with you that adb over wireless debugging is a bitch
because of Android security making certain you have access to the phone.

You can tell I get around stuff like that from my tutorial, but I have
never been able to get past that purposeful wireless-debugging security.

In the end, for others to know, adb/scrcpy works fine over USB or Wi-Fi,
but you must have physical access to the phone for the initial steps.

If you don't like scrcpy/sndcpy, you could try the free Vysor tool.
Vysor works with iOS & Android, while scrcpy works only with Android.

<https://i.postimg.cc/xdSMtBkn/vysor36.jpg> scrcpy vs Vysor resolution
<https://i.postimg.cc/TYvqdxCT/vysor35.jpg> iOS & Android PC mirroring
<https://i.postimg.cc/k5gv0yw8/vysor34.jpg> Apple iOS & Android mirroring
<https://i.postimg.cc/Njg6Xx3V/vysor33.jpg> Preparing Vysor on device
<https://i.postimg.cc/xjz3V8Gs/vysor32.jpg> ScrCpy vs Vysor PC mirror
<https://i.postimg.cc/k4K8dZqv/vysor31.jpg> Random MAC address is static
<https://i.postimg.cc/nchSVcmS/vysor30.jpg> Static/Reserved IP address
<https://i.postimg.cc/XqrD5Hqm/vysor29.jpg> Removing Apple iTunes crap
<https://i.postimg.cc/KYbVWDp3/vysor28.jpg> Nuking Apple shitware 1 by 1
<https://i.postimg.cc/MGbkZFfY/vysor27.jpg> The bloatware is everywhere
<https://i.postimg.cc/hP6R2xqV/vysor26.jpg> iTunes crapware won't install
<https://i.postimg.cc/fTy57WSY/vysor25.jpg> Best iOS drivers installed
<https://i.postimg.cc/3wmtyL46/vysor24.jpg> Apple Device working properly
<https://i.postimg.cc/tCvS8nGr/vysor23.jpg> iPad is connected to Win10
<https://i.postimg.cc/Kz7pW9mL/vysor22.jpg> Apple Win10 iOS drivers suck
<https://i.postimg.cc/QdVPMkqG/vysor21.jpg> Apple iPad on Win10 over USB
<https://i.postimg.cc/J7cSYhhg/vysor20.jpg> Classic Apple error 2502
<https://i.postimg.cc/yxP5DL5B/vysor19.jpg> Classic Apple error 2503
<https://i.postimg.cc/V6X28fWJ/vysor18.jpg> Apple Mobile Device Support
<https://i.postimg.cc/ZqB1wF9F/vysor17.jpg> Install Apple AMDS engine
<https://i.postimg.cc/Jzdf3dhz/vysor16.jpg> Classic Apple Error Code 2503
<https://i.postimg.cc/c4TyCJyY/vysor15.jpg> Apple Mobile Device Support
<https://i.postimg.cc/SRhF22xL/vysor14.jpg> Connect over the Internet
<https://i.postimg.cc/bv4jPFXB/vysor13.jpg> Vysor Camera virtual webcam
<https://i.postimg.cc/XvPnJY5x/vysor10.jpg> Vysor Windows Virtual Camera
<https://i.postimg.cc/wxL9qHjc/vysor11.jpg> Vysor searches for Android/iOS
<https://i.postimg.cc/2S2zsw8s/vysor09.jpg> Classic Apple Error code 2503
<https://i.postimg.cc/sg6r6gTy/vysor12.jpg> Vysor easily finds Android
<https://i.postimg.cc/yYCYcxbb/vysor08.jpg> Apple Mobile Device Support
<https://i.postimg.cc/Y2WCvYbF/vysor07.jpg> iOS requires Apple AMDS kluge
<https://i.postimg.cc/ydJYXZKw/vysor06.jpg> Remote mirror over the net
<https://i.postimg.cc/d0V03fxQ/vysor05.jpg> Vysor Internet mirroring
<https://i.postimg.cc/XY3qSqKC/vysor04.jpg> Vysor ADB USB setup switches
<https://i.postimg.cc/v8gc5pHc/vysor03.jpg> Vysor remote sharing
<https://i.postimg.cc/V6TPYG3h/vysor02.jpg> Vysor console operation
<https://i.postimg.cc/QNwjsCDM/vysor01.jpg> Vysor Android/iOS PC mirroring
--
On Usenet, everyone is an expert in something that they can impart to all.
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From Newsgroup: comp.mobile.android

Maria Sophia wrote:

a. Low end Android => use HeliBoard + WhisperIME STT
b. High end Android => try the all-in-one Futo Keyboard

Testing takes time...

I'm having trouble with the tiny models in a noisy environment,
with the transcription taking too long or not working at all.

It seems the AGC on the mic is allowing too much noise to filter through.

First, I confirmed the small models are running by running adb logcat.
"testing testing 123"

Since, at home, you never need to touch the phone itself, from Windows:
adb shell logcat -c
adb shell "logcat -d -v tag WhisperEngineJava:D *:S"
--------- beginning of main
D/WhisperEngineJava: Model is
loaded.../storage/emulated/0/Android/data/org.woheller69.whisper/files/whisper-tiny.en.tflite
D/WhisperEngineJava: Filters and Vocab are loaded.../storage/emulated/0/Android/data/org.woheller69.whisper/files/filters_vocab_en.bin
D/WhisperEngineJava: Model is loaded.../storage/emulated/0/Android/data/org.woheller69.whisper/files/whisper-tiny.en.tflite
D/WhisperEngineJava: Filters and Vocab are loaded.../storage/emulated/0/Android/data/org.woheller69.whisper/files/filters_vocab_en.bin

Where the specgtrogram was too big for such as small sentence:
D/WhisperEngineJava: Calculating Mel spectrogram...
D/WhisperEngineJava: Mel spectrogram is calculated...!
D/WhisperEngineJava: output_len: 449

So to lower the mic sensitivity on the Samsung A32-5G, I ran:
adb shell settings put global call_noise_reduction 1
adb reboot

Re-run "testing, testing, 123"
adb shell logcat -c
adb shell "logcat -d -v tag WhisperEngineJava:D *:S"
--------- beginning of main
D/WhisperEngineJava: Model is loaded.../storage/emulated/0/Android/data/org.woheller69.whisper/files/whisper-tiny.en.tflite
D/WhisperEngineJava: Filters and Vocab are loaded.../storage/emulated/0/Android/data/org.woheller69.whisper/files/filters_vocab_en.bin
D/WhisperEngineJava: Calculating Mel spectrogram...
D/WhisperEngineJava: Mel spectrogram is calculated...!
D/WhisperEngineJava: output_len: 449
D/WhisperEngineJava: Skipping token: 50257, word: [_SOT_]
D/WhisperEngineJava: Detected language code: en
D/WhisperEngineJava: Skipping token: 50259, word: [_extra_token_50259] D/WhisperEngineJava: It is Transcription...
D/WhisperEngineJava: Skipping token: 50359, word: [_extra_token_50359] D/WhisperEngineJava: Skipping token: 50363, word: [_BEG_]
D/WhisperEngineJava: Skipping token: 50413, word: [_TT_50]
D/WhisperEngineJava: Skipping token: 50513, word: [_TT_150] D/WhisperEngineJava: Inference is executed...!

Drat. It's still 449.

If that doesn't work in noisy environments, then I'll have to bump up
to the next-sized model, which I think is the base model.

adb push whisper-base.en.tflite /storage/emulated/0/Android/data/org.woheller69.whisper/files/
adb shell "cp /storage/emulated/0/Android/data/org.woheller69.whisper/files/whisper-base.en.tflite /storage/emulated/0/Android/data/org.woheller69.whisper/files/whisper.tflite"
adb shell "cp /storage/emulated/0/Android/data/org.woheller69.whisper/files/whisper-base.en.tflite /storage/emulated/0/Android/data/org.woheller69.whisper/files/whisper-tiny.en.tflite"
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From Newsgroup: comp.mobile.android

On Wed, 5/6/2026 10:37 PM, Maria Sophia wrote:

Maria Sophia wrote:

a. Low end Android => use HeliBoard + WhisperIME STT
b. High end Android => try the all-in-one Futo Keyboard

Testing takes time...

I'm having trouble with the tiny models in a noisy environment,
with the transcription taking too long or not working at all.

It seems the AGC on the mic is allowing too much noise to filter through.

First, I confirmed the small models are running by running adb logcat.
"testing testing 123"

Since, at home, you never need to touch the phone itself, from Windows:
adb shell logcat -c
adb shell "logcat -d -v tag WhisperEngineJava:D *:S"
--------- beginning of main
D/WhisperEngineJava: Model is
loaded.../storage/emulated/0/Android/data/org.woheller69.whisper/files/whisper-tiny.en.tflite
D/WhisperEngineJava: Filters and Vocab are loaded.../storage/emulated/0/Android/data/org.woheller69.whisper/files/filters_vocab_en.bin
D/WhisperEngineJava: Model is loaded.../storage/emulated/0/Android/data/org.woheller69.whisper/files/whisper-tiny.en.tflite
D/WhisperEngineJava: Filters and Vocab are loaded.../storage/emulated/0/Android/data/org.woheller69.whisper/files/filters_vocab_en.bin

Where the specgtrogram was too big for such as small sentence:
D/WhisperEngineJava: Calculating Mel spectrogram...
D/WhisperEngineJava: Mel spectrogram is calculated...!
D/WhisperEngineJava: output_len: 449

So to lower the mic sensitivity on the Samsung A32-5G, I ran:
adb shell settings put global call_noise_reduction 1
adb reboot

Re-run "testing, testing, 123"
adb shell logcat -c
adb shell "logcat -d -v tag WhisperEngineJava:D *:S"
--------- beginning of main
D/WhisperEngineJava: Model is loaded.../storage/emulated/0/Android/data/org.woheller69.whisper/files/whisper-tiny.en.tflite
D/WhisperEngineJava: Filters and Vocab are loaded.../storage/emulated/0/Android/data/org.woheller69.whisper/files/filters_vocab_en.bin
D/WhisperEngineJava: Calculating Mel spectrogram...
D/WhisperEngineJava: Mel spectrogram is calculated...!
D/WhisperEngineJava: output_len: 449
D/WhisperEngineJava: Skipping token: 50257, word: [_SOT_] D/WhisperEngineJava: Detected language code: en
D/WhisperEngineJava: Skipping token: 50259, word: [_extra_token_50259] D/WhisperEngineJava: It is Transcription...
D/WhisperEngineJava: Skipping token: 50359, word: [_extra_token_50359] D/WhisperEngineJava: Skipping token: 50363, word: [_BEG_] D/WhisperEngineJava: Skipping token: 50413, word: [_TT_50] D/WhisperEngineJava: Skipping token: 50513, word: [_TT_150] D/WhisperEngineJava: Inference is executed...!

Drat. It's still 449.

If that doesn't work in noisy environments, then I'll have to bump up
to the next-sized model, which I think is the base model.

adb push whisper-base.en.tflite /storage/emulated/0/Android/data/org.woheller69.whisper/files/
adb shell "cp /storage/emulated/0/Android/data/org.woheller69.whisper/files/whisper-base.en.tflite /storage/emulated/0/Android/data/org.woheller69.whisper/files/whisper.tflite"
adb shell "cp /storage/emulated/0/Android/data/org.woheller69.whisper/files/whisper-base.en.tflite /storage/emulated/0/Android/data/org.woheller69.whisper/files/whisper-tiny.en.tflite"

It missed the word "It's" in the picture.

[Picture] dsnote-ubu2504.gif

https://imgur.com/a/9VxuCCa

https://postimg.cc/CRrHVQXP

That's "dsnote" in Ubuntu using a Whisper model.
I read the text of the lines above, and the model
missed the "It's" on the recorded attempt. A
previous attempt was OK.

Microphone was a Blue Yeti. Which doesn't have AGC.
And the level wasn't all that high either, maybe
-24dBm or so. I recorded the microphone first in
Audacity, to see I had to hold the mike two inches
from my face to get a signal.

While the spec for the microphone claims a 20-20000Hz
response (which would be 3dB down at the ends),
it is clearly a "voice" microphone and it
cuts off the high frequencies. That's one of the reasons
the fans in the room didn't get picked up. So as far as
being a "live" mic, it's a bit of a "dull potato" as
mics go. But it does seem to give a decent result.

And when you "blast" the four lines above at the model,
then stop and wait for the conversion, it must have taken
at least 10-15 seconds to do the amount of text in the picture.
It "feels" slightly better, if you feed it a sentence at a time.
Feed it just a few words. It seems happier that way. Dragon
Naturally Speaking has nothing to worry about :-)

Paul
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From Newsgroup: comp.mobile.android

Paul wrote:

That's "dsnote" in Ubuntu using a Whisper model.
I read the text of the lines above, and the model
missed the "It's" on the recorded attempt. A
previous attempt was OK.

Microphone was a Blue Yeti. Which doesn't have AGC.
And the level wasn't all that high either, maybe
-24dBm or so. I recorded the microphone first in
Audacity, to see I had to hold the mike two inches
from my face to get a signal.

While the spec for the microphone claims a 20-20000Hz
response (which would be 3dB down at the ends),
it is clearly a "voice" microphone and it
cuts off the high frequencies. That's one of the reasons
the fans in the room didn't get picked up. So as far as
being a "live" mic, it's a bit of a "dull potato" as
mics go. But it does seem to give a decent result.

And when you "blast" the four lines above at the model,
then stop and wait for the conversion, it must have taken
at least 10-15 seconds to do the amount of text in the picture.
It "feels" slightly better, if you feed it a sentence at a time.
Feed it just a few words. It seems happier that way. Dragon
Naturally Speaking has nothing to worry about :-)

Hi Paul,

Thanks for testing it out. I think there's a reason that the WhisperIME defaults to the 435MB model instead of the "tiny" model of 40MB.

I agree with EVERYTHING you said (I'd never disagree with anything that is logically sensibly stated). What I want to say is that when it's quiet, it works "just OK" for the tiny model.

I'm gonna switch to the default 435MB model and see if that does better.
But I agree with you. YMMV.

When it's noisy (like in a vehicle), the tiny model really sucks.
So I guess we're doomed to have to use the largest model most of the time.

I'm told (by the Internet) that the Futo Keyboard works better as it's more modern and it uses the C++ whisper models (if that matters).

If I were to do it over again, I'd try that first.

But I do THANK YOU VERY MUCH for testing this out for the team.
People like you are wonderful because we all benefit from your efforts!

What's really neat is that Windows/Linux controls the phone wonderfully.
I never have to touch the phone when I'm sitting at my desk.
a. adb controls the phone
b. scrcpy/sndcpy displays the phone
c. the keyboard types into the phone
d. the mouse taps on the phone

It's really neat having the phone show up as nearly two feet tall!
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