Fix #130 Voice consistency in documentation (#143)

* documentation of voice consistencies

* fix typos

Author: bhargavyagnik

INFERENCE.md

@@ -7,6 +7,7 @@ Parler-TTS benefits from a number of optimizations that can make the model up to
 * [Compilation](#compilation)
 * [Streaming](#streaming)
 * [Batch generation](#batch-generation)
+* [Speaker Consistency](#speaker-consistency)
 
 ## Efficient Attention implementations
 
@@ -199,4 +200,65 @@ audio_2 = generation.sequences[1, :generation.audios_length[1]]
 print(audio_1.shape, audio_2.shape)
 scipy.io.wavfile.write("sample_out.wav", rate=feature_extractor.sampling_rate, data=audio_1.cpu().numpy().squeeze())
 scipy.io.wavfile.write("sample_out_2.wav", rate=feature_extractor.sampling_rate, data=audio_2.cpu().numpy().squeeze())
-```
+```
+
+## Speaker Consistency
+
+The checkpoint was trained on 34 speakers. The full list of available speakers includes:
+Laura, Gary, Jon, Lea, Karen, Rick, Brenda, David, Eileen, Jordan, Mike, Yann, Joy, James, Eric, Lauren, Rose, Will, Jason, Aaron, Naomie, Alisa, Patrick, Jerry, Tina, Jenna, Bill, Tom, Carol, Barbara, Rebecca, Anna, Bruce, and Emily.
+
+However, the models performed better with certain speakers. Below are the top 20 speakers for each model variant, ranked by their average speaker similarity scores:
+
+### Large Model - Top 20 Speakers
+
+| Speaker | Similarity Score |
+|---------|------------------|
+| Will    | 0.906055         |
+| Eric    | 0.887598         |
+| Laura   | 0.877930         |
+| Alisa   | 0.877393         |
+| Patrick | 0.873682         |
+| Rose    | 0.873047         |
+| Jerry   | 0.871582         |
+| Jordan  | 0.870703         |
+| Lauren  | 0.867432         |
+| Jenna   | 0.866455         |
+| Karen   | 0.866309         |
+| Rick    | 0.863135         |
+| Bill    | 0.862207         |
+| James   | 0.856934         |
+| Yann    | 0.856787         |
+| Emily   | 0.856543         |
+| Anna    | 0.848877         |
+| Jon     | 0.848828         |
+| Brenda  | 0.848291         |
+| Barbara | 0.847998         |
+
+### Mini Model - Top 20 Speakers
+
+| Speaker | Similarity Score |
+|---------|------------------|
+| Jon     | 0.908301         |
+| Lea     | 0.904785         |
+| Gary    | 0.903516         |
+| Jenna   | 0.901807         |
+| Mike    | 0.885742         |
+| Laura   | 0.882666         |
+| Lauren  | 0.878320         |
+| Eileen  | 0.875635         |
+| Alisa   | 0.874219         |
+| Karen   | 0.872363         |
+| Barbara | 0.871509         |
+| Carol   | 0.863623         |
+| Emily   | 0.854932         |
+| Rose    | 0.852246         |
+| Will    | 0.851074         |
+| Patrick | 0.850977         |
+| Eric    | 0.845459         |
+| Rick    | 0.845020         |
+| Anna    | 0.844922         |
+| Tina    | 0.839160         |
+
+The numbers represent the average speaker similarity between a random snippet of the person speaking and a randomly Parler-generated snippet. Higher scores indicate better model performance in maintaining voice consistency.
+
+These scores are derived from [dataset for Mini](https://huggingface.co/datasets/ylacombe/parler-tts-mini-v1_speaker_similarity) and [dataset for Large](https://huggingface.co/datasets/ylacombe/parler-large-v1-og_speaker_similarity).

README.md

@@ -79,10 +79,13 @@ sf.write("parler_tts_out.wav", audio_arr, model.config.sampling_rate)
 
 ### 🎯 Using a specific speaker
 
-To ensure speaker consistency across generations, this checkpoint was also trained on 34 speakers, characterized by name (e.g. Jon, Lea, Gary, Jenna, Mike, Laura).
+To ensure speaker consistency across generations, this checkpoint was also trained on 34 speakers, characterized by name. The full list of available speakers includes:
+Laura, Gary, Jon, Lea, Karen, Rick, Brenda, David, Eileen, Jordan, Mike, Yann, Joy, James, Eric, Lauren, Rose, Will, Jason, Aaron, Naomie, Alisa, Patrick, Jerry, Tina, Jenna, Bill, Tom, Carol, Barbara, Rebecca, Anna, Bruce, Emily.
 
 To take advantage of this, simply adapt your text description to specify which speaker to use: `Jon's voice is monotone yet slightly fast in delivery, with a very close recording that almost has no background noise.`
 
+You can replace "Jon" with any of the names from the list above to utilize different speaker characteristics. Each speaker has unique vocal qualities that can be leveraged to suit your specific needs. For more detailed information on speaker performance with voice consistency, please refer [inference guide](INFERENCE.md#speaker-consistency).
+
 ```py
 import torch
 from parler_tts import ParlerTTSForConditionalGeneration