From 272eb8f7aab1c5396bdf3413dd71e415b4f780d8 Mon Sep 17 00:00:00 2001
From: tarepan <tarepan5884@gmail.com>
Date: Mon, 4 Sep 2023 06:27:36 +0000
Subject: [PATCH] Add release v1.0.0

---
 README.md      | 17 +++++++++--------
 hubconf.py     |  2 +-
 pyproject.toml |  2 +-
 3 files changed, 11 insertions(+), 10 deletions(-)

diff --git a/README.md b/README.md
index db6c7f1..feff8ba 100644
--- a/README.md
+++ b/README.md
@@ -7,9 +7,9 @@
 Predict subjective speech score with only 2 lines of code, with various MOS prediction systems.
 
 ```python
-predictor = torch.hub.load("tarepan/SpeechMOS:main", "utmos22_strong", trust_repo=True)
-score = predictor(wave, sample_rate)
-# xx, good quality speech!
+predictor = torch.hub.load("tarepan/SpeechMOS:v1.0.0", "utmos22_strong", trust_repo=True)
+score = predictor(wave, sr)
+# tensor([3.7730]), good quality speech!
 ```
 
 ## Demo
@@ -20,9 +20,9 @@ import torch
 import librosa
 
 wave, sr = librosa.load("<your_audio>.wav", sr=None, mono=True)
-predictor = torch.hub.load("tarepan/SpeechMOS:main", "utmos22_strong", trust_repo=True)
+predictor = torch.hub.load("tarepan/SpeechMOS:v1.0.0", "utmos22_strong", trust_repo=True)
 score = predictor(torch.from_numpy(wave).unsqueeze(0), sr)
-# 
+# tensor([3.7730])
 ```
 
 ## How to Use
@@ -31,21 +31,22 @@ SpeechMOS use `torch.hub` built-in model loader, so no needs of library import
 
 First, instantiate a MOS predictor with model specifier string:
 ```python
-predictor = torch.hub.load("tarepan/SpeechMOS:main", "<model_specifier>", trust_repo=True)
+import torch
+predictor = torch.hub.load("tarepan/SpeechMOS:v1.0.0", "<model_specifier>", trust_repo=True)
 ```
 
 Then, pass tensor of speeches :: `(Batch, Time)`:
 ```python
 waves_tensor = torch.rand((2, 16000)) # Two speeches, each 1 sec (sr=16,000)
 score = predictor(waves_tensor, sr=16000)
-#
+# tensor([2.0321, 2.0943])
 ```
 
 Returned scores :: `(Batch,)` are each speech's predicted MOS.  
 If you hope MOS average over speeches (e.g. for TTS model evaluation), just average them:
 ```python
 average_score = score.mean().item()
-#
+# 2.0632
 ```
 
 ## Predictors
diff --git a/hubconf.py b/hubconf.py
index f12fe4b..5442a38 100644
--- a/hubconf.py
+++ b/hubconf.py
@@ -8,7 +8,7 @@
 
 
 URLS = {
-    "utmos22_strong": "https://github.com/tarepan/SpeechMOS/releases/download/v0.0.0/utmos22_strong_step7459.pt",
+    "utmos22_strong": "https://github.com/tarepan/SpeechMOS/releases/download/v1.0.0/utmos22_strong_step7459_v1.pt",
 }
 # [Origin]
 # "utmos22_strong" is derived from official sarulab-speech/UTMOS22 'UTMOS strong learner' checkpoint, under MIT lisence (Copyright 2022 Saruwatari&Koyama laboratory, The University of Tokyo, https://github.com/sarulab-speech/UTMOS22/blob/master/LICENSE).
diff --git a/pyproject.toml b/pyproject.toml
index d5b32e3..27a8bec 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "speechmos"
-version = "0.0.1"
+version = "1.0.0"
 description = "Easy-to-Use Speech MOS predictors 🎧"
 authors = ["tarepan"]
 readme = "README.md"