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README.md

@@ -51,7 +51,7 @@ model-index:
 
 - **About as accurate as Whisper small on Turkish**, with 50× fewer parameters and streaming output: 11.24 WER on FLEURS-TR test against 11.87 for Whisper small, and 16.88 against 23.25 on broadcast audio (MediaSpeech).
 - **Better than seda-v0.1** (66M parameters, 11.60 WER) at a fifteenth of its size.
-- **Runs in a phone's browser:** with sherpa-onnx WASM on one thread of a low-end Android phone (Samsung Galaxy A04s), the 0.64 s chunk model with beam search runs at 0.77× real time.
+- **Runs in a phone's browser:** with sherpa-onnx WASM on one thread of a low-end Android phone, the 0.64 s chunk model with beam search runs at 0.77× real time.
 - The same language model, voice activity detector and text formatter setup as seda-v0.2, so every [sedalib](https://github.com/atasoglu/seda) mode works.
 
 ## Usage
@@ -102,7 +102,7 @@ Shorter chunks react sooner but cost more CPU per second of audio and are a litt
 | 1.28 s (`chunk=64`) | root | 16.50 | 11.24 |
 
 - **Measurement:** sherpa-onnx int8 on CPU, audio followed by 1 s of silence. The `accurate` row decodes each utterance with full context (a 20.48 s chunk) and beam 4 + LM. The 0.16 and 0.32 s rows were measured with 0.64 s of left context; the released `c8/` and `c16/` use 1.28 s, which was 0.5 to 0.8 WER better on FLEURS dev.
-- **Slow devices:** on the Galaxy A04s, 0.16 s chunks with beam search needed 1.07 s of compute per second of audio and fell behind; 0.32 s greedy needed 0.89 s and 0.64 s with beam search 0.77 s. Use 0.32 or 0.64 s chunks there; 0.16 s suits fast CPUs where the earliest reaction matters.
+- **Slow devices:** on a low-end Android phone (sherpa-onnx WASM, one thread), 0.16 s chunks with beam search needed 1.07 s of compute per second of audio and fell behind; 0.32 s greedy needed 0.89 s and 0.64 s with beam search 0.77 s. Use 0.32 or 0.64 s chunks there; 0.16 s suits fast CPUs where the earliest reaction matters.
 
 ## Evaluation