Clarify and wrap.

README.md

@@ -15,16 +15,36 @@ The HelloSippyRT model is designed to adapt Microsoft's SpeechT5 Text-to-Speech
 
 ## Problem Statement
 
-The original vocoder performs optimally only when provided with almost the full Text-To-Mel ("TTM") sequence at once. This is not ideal for real-time applications, where we aim to begin audio output quickly. Using smaller chunks results in "clicking" distortions between adjacent audio frames. Fine-tuning attempts on Microsoft's HiFiGAN vocoder were unsuccessful.
+The original vocoder performs optimally only when provided with almost the full Mel sequence produced from the single
+text input at once. This is not ideal for real-time applications, where we aim to begin audio output quickly.
+Using smaller chunks results in "clicking" distortions between adjacent audio frames.
+Fine-tuning attempts on Microsoft's HiFiGAN vocoder were unsuccessful.
 
 ## Solution
 
-Our approach involves a smaller model that takes a fixed audio chunk of 8 Mel frames, two pre-frames, and two post-frames. These frames are processed along with the original vocoder's 12 audio frames of 256 bytes each. The model employs convolution input layers for both audio and Mel frames to generate hidden dimensions, followed by two linear layers and a final convolution layer. The output is then multiplied with the original 8 audio frames to produce corrected frames.
+Our approach involves a smaller model that takes a fixed audio chunk of 8 Mel frames, two pre-frames, and two post-frames.
+These frames are processed along with the original vocoder's 12 audio frames of 256 bytes each. The model employs
+convolution input layers for both audio and Mel frames to generate hidden dimensions, followed by two linear layers and
+a final convolution layer. The output is then multiplied with the original 8 audio frames to produce corrected frames.
+
 ![HelloSippyRT Model Architecture](https://docs.google.com/drawings/d/e/2PACX-1vTiWxGbEB2MbvHpTJHS22abWNrSt2pHv6XijEDmnQFjAqBewMJyZBQ_5Y9k1P9INQPQmuq56MpLDzJt/pub?w=960&h=720)
 
 ## Training Details
 
-We trained the model using a subset of 3,000 audio utterances from the `LJSpeech-1.1` dataset. The original SpeechT5 TTS module generated the voice using speakers randomly selected from the `Matthijs/cmu-arctic-xvectors` dataset. During training, the original vocoder was locked; only our model was fine-tuned to mimic the original vocoder as closely as possible in continuous mode.
+We trained the model using a subset of 3,000 audio utterances from the `LJSpeech-1.1` dataset. The SpeechT5's Speech-To-Speech
+module was employed to replace voice in each utterance with a voice of speakers randomly selected from the
+`Matthijs/cmu-arctic-xvectors` dataset. Such produced reference Mel spectrum were used to feed vocoder and post-vocoder
+in chunks. The FFT of generated in "continuous" mode reference waveform was used as a basis for loss-function calculation.
+
+During training, the original vocoder was locked; only our model was trained to mimic the original vocoder as closely as
+possible in continuous mode.
+
+## Evaluation
+
+The model has been evaluated by producing TTS output from pure text input using quotes from the "Futurama", "Martix" and
+"Space Odyssey 2001" retrieved from the wikiquotes site using purely random speaker vector as well as vectors from the
+`Matthijs/cmu-arctic-xvectors` dataset. The quality of output has been found satisfactory for our particular
+use. 
 
 ## Source Code & Links