fix: correct comments

fix: correct comments

Author: Mikemosca

lib/ibm_watson/speech_to_text_v1.rb

@@ -277,14 +277,8 @@ def get_model(model_id:)
     # @param keywords [Array[String]] An array of keyword strings to spot in the audio. Each keyword string can include
     #   one or more string tokens. Keywords are spotted only in the final results, not in
     #   interim hypotheses. If you specify any keywords, you must also specify a keywords
-    #   threshold. Omit the parameter or specify an empty array if you do not need to spot
-    #   keywords.
-    #
-    #   You can spot a maximum of 1000 keywords with a single request. A single keyword
-    #   can have a maximum length of 1024 characters, though the maximum effective length
-    #   for double-byte languages might be shorter. Keywords are case-insensitive.
-    #
-    #   See [Keyword
+    #   threshold. You can spot a maximum of 1000 keywords. Omit the parameter or specify
+    #   an empty array if you do not need to spot keywords. See [Keyword
     #   spotting](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-output#keyword_spotting).
     # @param keywords_threshold [Float] A confidence value that is the lower bound for spotting a keyword. A word is
     #   considered to match a keyword if its confidence is greater than or equal to the
@@ -909,14 +903,8 @@ def unregister_callback(callback_url:)
     # @param keywords [Array[String]] An array of keyword strings to spot in the audio. Each keyword string can include
     #   one or more string tokens. Keywords are spotted only in the final results, not in
     #   interim hypotheses. If you specify any keywords, you must also specify a keywords
-    #   threshold. Omit the parameter or specify an empty array if you do not need to spot
-    #   keywords.
-    #
-    #   You can spot a maximum of 1000 keywords with a single request. A single keyword
-    #   can have a maximum length of 1024 characters, though the maximum effective length
-    #   for double-byte languages might be shorter. Keywords are case-insensitive.
-    #
-    #   See [Keyword
+    #   threshold. You can spot a maximum of 1000 keywords. Omit the parameter or specify
+    #   an empty array if you do not need to spot keywords. See [Keyword
     #   spotting](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-output#keyword_spotting).
     # @param keywords_threshold [Float] A confidence value that is the lower bound for spotting a keyword. A word is
     #   considered to match a keyword if its confidence is greater than or equal to the
@@ -1612,10 +1600,10 @@ def list_corpora(customization_id:)
     #
     #   The call returns an HTTP 201 response code if the corpus is valid. The service
     #   then asynchronously processes the contents of the corpus and automatically
-    #   extracts new words that it finds. This operation can take on the order of minutes
-    #   to complete depending on the total number of words and the number of new words in
-    #   the corpus, as well as the current load on the service. You cannot submit requests
-    #   to add additional resources to the custom model or to train the model until the
+    #   extracts new words that it finds. This can take on the order of a minute or two to
+    #   complete depending on the total number of words and the number of new words in the
+    #   corpus, as well as the current load on the service. You cannot submit requests to
+    #   add additional resources to the custom model or to train the model until the
     #   service's analysis of the corpus for the current request completes. Use the **List
     #   a corpus** method to check the status of the analysis.
     #
@@ -2160,12 +2148,12 @@ def list_grammars(customization_id:)
     #
     #   The call returns an HTTP 201 response code if the grammar is valid. The service
     #   then asynchronously processes the contents of the grammar and automatically
-    #   extracts new words that it finds. This operation can take a few seconds or minutes
-    #   to complete depending on the size and complexity of the grammar, as well as the
-    #   current load on the service. You cannot submit requests to add additional
-    #   resources to the custom model or to train the model until the service's analysis
-    #   of the grammar for the current request completes. Use the **Get a grammar** method
-    #   to check the status of the analysis.
+    #   extracts new words that it finds. This can take a few seconds to complete
+    #   depending on the size and complexity of the grammar, as well as the current load
+    #   on the service. You cannot submit requests to add additional resources to the
+    #   custom model or to train the model until the service's analysis of the grammar for
+    #   the current request completes. Use the **Get a grammar** method to check the
+    #   status of the analysis.
     #
     #   The service populates the model's words resource with any word that is recognized
     #   by the grammar that is not found in the model's base vocabulary. These are
@@ -2512,7 +2500,7 @@ def delete_acoustic_model(customization_id:)
     #   to complete depending on the total amount of audio data on which the custom
     #   acoustic model is being trained and the current load on the service. Typically,
     #   training a custom acoustic model takes approximately two to four times the length
-    #   of its audio data. The actual time depends on the model being trained and the
+    #   of its audio data. The range of time depends on the model being trained and the
     #   nature of the audio, such as whether the audio is clean or noisy. The method
     #   returns an HTTP 200 response code to indicate that the training process has begun.
     #
@@ -2531,9 +2519,8 @@ def delete_acoustic_model(customization_id:)
     #   Train with a custom language model if you have verbatim transcriptions of the
     #   audio files that you have added to the custom model or you have either corpora
     #   (text files) or a list of words that are relevant to the contents of the audio
-    #   files. For training to succeed, both of the custom models must be based on the
-    #   same version of the same base model, and the custom language model must be fully
-    #   trained and available.
+    #   files. Both of the custom models must be based on the same version of the same
+    #   base model for training to succeed.
     #
     #   **See also:**
     #   * [Train the custom acoustic
@@ -2549,9 +2536,6 @@ def delete_acoustic_model(customization_id:)
     #   another training request or a request to add audio resources to the model.
     #   * The custom model contains less than 10 minutes or more than 200 hours of audio
     #   data.
-    #   * You passed a custom language model with the `custom_language_model_id` query
-    #   parameter that is not in the available state. A custom language model must be
-    #   fully trained and available to be used to train a custom acoustic model.
     #   * You passed an incompatible custom language model with the
     #   `custom_language_model_id` query parameter. Both custom models must be based on
     #   the same version of the same base model.
@@ -2567,8 +2551,8 @@ def delete_acoustic_model(customization_id:)
     #   been trained with verbatim transcriptions of the audio resources or that contains
     #   words that are relevant to the contents of the audio resources. The custom
     #   language model must be based on the same version of the same base model as the
-    #   custom acoustic model, and the custom language model must be fully trained and
-    #   available. The credentials specified with the request must own both custom models.
+    #   custom acoustic model. The credentials specified with the request must own both
+    #   custom models.
     # @return [IBMCloudSdkCore::DetailedResponse] A `IBMCloudSdkCore::DetailedResponse` object representing the response.
     def train_acoustic_model(customization_id:, custom_language_model_id: nil)
       raise ArgumentError.new("customization_id must be provided") if customization_id.nil?
@@ -2666,9 +2650,8 @@ def reset_acoustic_model(customization_id:)
     #   service that owns the custom model.
     # @param custom_language_model_id [String] If the custom acoustic model was trained with a custom language model, the
     #   customization ID (GUID) of that custom language model. The custom language model
-    #   must be upgraded before the custom acoustic model can be upgraded. The custom
-    #   language model must be fully trained and available. The credentials specified with
-    #   the request must own both custom models.
+    #   must be upgraded before the custom acoustic model can be upgraded. The credentials
+    #   specified with the request must own both custom models.
     # @param force [Boolean] If `true`, forces the upgrade of a custom acoustic model for which no input data
     #   has been modified since it was last trained. Use this parameter only to force the
     #   upgrade of a custom acoustic model that is trained with a custom language model,
@@ -2763,14 +2746,14 @@ def list_audio(customization_id:)
     #   same name as an existing audio resource, set the `allow_overwrite` parameter to
     #   `true`; otherwise, the request fails.
     #
-    #   The method is asynchronous. It can take several seconds or minutes to complete
-    #   depending on the duration of the audio and, in the case of an archive file, the
-    #   total number of audio files being processed. The service returns a 201 response
-    #   code if the audio is valid. It then asynchronously analyzes the contents of the
-    #   audio file or files and automatically extracts information about the audio such as
-    #   its length, sampling rate, and encoding. You cannot submit requests to train or
-    #   upgrade the model until the service's analysis of all audio resources for current
-    #   requests completes.
+    #   The method is asynchronous. It can take several seconds to complete depending on
+    #   the duration of the audio and, in the case of an archive file, the total number of
+    #   audio files being processed. The service returns a 201 response code if the audio
+    #   is valid. It then asynchronously analyzes the contents of the audio file or files
+    #   and automatically extracts information about the audio such as its length,
+    #   sampling rate, and encoding. You cannot submit requests to train or upgrade the
+    #   model until the service's analysis of all audio resources for current requests
+    #   completes.
     #
     #   To determine the status of the service's analysis of the audio, use the **Get an
     #   audio resource** method to poll the status of the audio. The method accepts the