typos and minor fixes (#2508)

* Update tacotron1-2.md

* Update README.md

* Update Tutorial_2_train_your_first_TTS_model.ipynb

* Update synthesizer.py

There is no arg called --speaker_name

* Update formatting_your_dataset.md

* Update AnalyzeDataset.ipynb

* Update AnalyzeDataset.ipynb

* Update AnalyzeDataset.ipynb

* Update finetuning.md

* Update train_yourtts.py

* Update train_yourtts.py

* Update train_yourtts.py

* Update finetuning.md

docs/source/finetuning.md

@@ -11,7 +11,7 @@ them and fine-tune it for your own dataset. This will help you in two main ways:
     Since a pre-trained model has already learned features that are relevant for the task, it will converge faster on
     a new dataset. This will reduce the cost of training and let you experiment faster.
 
-2. Better resutls with small datasets
+2. Better results with small datasets
 
     Deep learning models are data hungry and they give better performance with more data. However, it is not always
     possible to have this abundance, especially in specific domains. For instance, the LJSpeech dataset, that we released most of
@@ -19,7 +19,7 @@ them and fine-tune it for your own dataset. This will help you in two main ways:
     the help of a voice actor.
 
     Fine-tuning comes to the rescue in this case. You can take one of our pre-trained models and fine-tune it on your own
-    speech dataset and achive reasonable results with only a couple of hours of data.
+    speech dataset and achieve reasonable results with only a couple of hours of data.
 
     However, note that, fine-tuning does not ensure great results. The model performance is still depends on the
     {ref}`dataset quality <what_makes_a_good_dataset>` and the hyper-parameters you choose for fine-tuning. Therefore,
@@ -35,7 +35,7 @@ them and fine-tune it for your own dataset. This will help you in two main ways:
 
 2. Choose the model you want to fine-tune.
 
-    You can list the availabe models in the command line with
+    You can list the available models in the command line with
 
     ```bash
     tts --list_models

docs/source/formatting_your_dataset.md

@@ -125,4 +125,4 @@ See `TTS.tts.datasets.TTSDataset`, a generic `Dataset` implementation for the `t
 See `TTS.vocoder.datasets.*`, for different `Dataset` implementations for the `vocoder` models.
 
 See `TTS.utils.audio.AudioProcessor` that includes all the audio processing and feature extraction functions used in a
-`Dataset` implementation. Feel free to add things as you need.passed
+`Dataset` implementation. Feel free to add things as you need.

docs/source/models/tacotron1-2.md

@@ -12,7 +12,7 @@ Vanilla Tacotron models are slow at inference due to the auto-regressive* nature
 
 Tacotron also uses a Prenet module with Dropout that projects the model’s previous output before feeding it to the decoder again. The paper and most of the implementations use the Dropout layer even in inference and they report the attention fails or the voice quality degrades otherwise. But the issue with that, you get a slightly different output speech every time you run the model.
 
-Tsraining the attention is notoriously problematic in Tacoron models. Especially, in inference, for some input sequences, the alignment fails and causes the model to produce unexpected results. There are many different methods proposed to improve the attention.
+Training the attention is notoriously problematic in Tacoron models. Especially, in inference, for some input sequences, the alignment fails and causes the model to produce unexpected results. There are many different methods proposed to improve the attention.
 
 After hundreds of experiments,  @ 🐸TTS we suggest Double Decoder Consistency that leads to the most robust model performance.