Audio: MFCC: Add Python script for speech to text with Whisper model

Add sof_mel_to_text_live_dsp_vad.py that captures mel spectrogram frames
from ALSA with embedded DSP VAD flag and performs live speech-to-text
transcription using OpenVINO Whisper. The script buffers mel frames during
speech and triggers Whisper inference when silence is detected after
speech. Capture runs continuously in a separate thread during inference
to avoid frame drops.

Replace the old README.txt with a comprehensive README.md that documents
the MFCC tuning tools, testbench usage with run_mfcc.sh, output file
formats, Matlab/Octave decode and plotting scripts, and the new live
transcription workflow.

Signed-off-by: Seppo Ingalsuo <seppo.ingalsuo@linux.intel.com>

Author: singalsu

src/audio/mfcc/tune/README.md

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+# SOF MFCC Tuning Tools
+
+This directory contains a tool to create configuration blob for SOF
+MFCC component. It's simply run in Matlab or Octave with command
+`setup_mfcc`. The MFCC configuration parameters can be edited from the
+script.
+
+## Testbench
+
+The configuration can be test run with testbench. First the test topologies
+need to be created with `scripts/build-tools.sh -t`. Next the testbench
+is built with `scripts/rebuild-testbench.sh`.
+
+Once the previous steps are done, a sample wav file can be processed
+with script `run_mfcc.sh`. The script converts the input to raw 16 kHz
+stereo format and runs the testbench for S16, S24, and S32 bit depths,
+producing both cepstral coefficient (MFCC) and Mel spectrogram outputs.
+
+```
+./run_mfcc.sh /usr/share/sounds/alsa/Front_Center.wav
+```
+
+Output files from host testbench:
+
+| File | Content |
+|------|---------|
+| `mfcc_s16.raw`, `mfcc_s24.raw`, `mfcc_s32.raw` | Cepstral coefficients |
+| `mel_s16.raw`, `mel_s24.raw`, `mel_s32.raw` | Mel spectrogram |
+
+If the `XTENSA_PATH` environment variable is set, the script also runs
+the Xtensa build of the testbench (via `xt-run`) and produces additional
+output files prefixed with `xt_`:
+
+| File | Content |
+|------|---------|
+| `xt_mfcc_s16.raw`, `xt_mfcc_s24.raw`, `xt_mfcc_s32.raw` | Cepstral coefficients |
+| `xt_mel_s16.raw`, `xt_mel_s24.raw`, `xt_mel_s32.raw` | Mel spectrogram |
+
+## Decoding and Plotting
+
+All output files can be decoded and plotted at once in Matlab or Octave
+with the `decode_all.m` script:
+
+```matlab
+decode_all
+```
+
+This calls `decode_ceps` for each MFCC file (13 cepstral coefficients) and
+`decode_mel` for each Mel file (80 Mel bins), plotting spectrograms for all
+files that exist including the Xtensa variants.
+
+Individual files can also be decoded manually:
+
+```matlab
+[ceps, t, n] = decode_ceps('mfcc_s16.raw', 13);
+```
+
+In the above it's known from configuration script that MFCC was set up to
+output 13 cepstral coefficients from each FFT → Mel → DCT → Cepstral
+coefficients computation run.
+
+The 80 bands Mel output can be visualized with command:
+
+```matlab
+[mel, t, n] = decode_mel('mel_s16.raw', 80);
+```
+
+## Live Whisper Transcription with DSP VAD
+
+The directory contains a Python script `sof_mel_to_text_live_dsp_vad.py`.
+It can be used with development topologies
+`sof-arl-cs42l43-l0-cs35l56-l23-mfcc.tplg` and
+`sof-mtl-rt713-l0-rt1316-l12-mfcc.tplg`. It captures from default audio
+device `hw:0,47` (headset microphone) Mel audio features and VAD flags.
+The captured frames with detected speech are sent to Whisper speech
+recognizer model for conversion to text.
+
+### Prerequisites
+
+The script needs OpenVINO. Please follow the install procedure from
+<https://docs.openvino.ai/2025/get-started/install-openvino.html>.
+
+The following Python pip installs are needed into the same OpenVINO venv:
+
+```bash
+pip install openvino openvino-tokenizers openvino-genai
+pip install optimum[intel]
+pip install transformers
+pip install huggingface_hub
+```
+
+### NPU / GPU Support
+
+The script by default runs the Whisper encoder model in the NPU. To
+use the NPU, install the driver from
+<https://github.com/intel/linux-npu-driver/releases>. If the NPU is not
+available, change the encoder to CPU with run option `--encoder-device CPU`.
+With a GPU both `--encoder-device GPU` and `--decoder-device GPU` can be set.