typos

Author: hendrytl

examples/evaluation/use-cases/EvalsAPI_Audio_Inputs.ipynb

@@ -8,7 +8,7 @@
     "\n",
     "This cookbook demonstrates how to use OpenAI's Evals framework for audio-based tasks. Leveraging the Evals API, we will grade model-generated responses to an audio message and prompt by using **sampling** to generate model responses and **string match grading** to score the model responses against the output audio transcript and reference answer. Note that grading will be on text outputs from the sampled response. Graders that can grade audio input are not currently supported.\n",
     "\n",
-    "Before audio support was added, in order to evaluate audio conversations, they needed to be first transcribed to text.  Now you can use the original audio and get samples from the model in audio as well. This will more accurately repesent workflows such as a customer suppor agent where both the user and agent are using aduio. For grading, we use the text transcript from the sampled audio so that we can leverage the existig suite of text graders. \n",
+    "Before audio support was added, in order to evaluate audio conversations, they needed to be first transcribed to text.  Now you can use the original audio and get samples from the model in audio as well. This will more accurately represent workflows such as a customer support agent where both the user and agent are using audio. For grading, we use the text transcript from the sampled audio so that we can leverage the existing suite of text graders. \n",
     "\n",
     "In this example, we will evaluate how well our model can:\n",
     "1. **Generate appropriate responses** to user prompts about an audio message\n",
@@ -57,7 +57,7 @@
    "source": [
     "## Dataset Preparation\n",
     "\n",
-    "We use the [big_bench_audio](https://huggingface.co/datasets/ArtificialAnalysis/big_bench_audio) dataset that's hosted on Hugging Face. Big Bench Audio is an audio version of a subset of Big Bench Hard questions. The dataset can be used for evaluating the reasoning capabilities of models that support audio input. It contains an audio clip describing a logic problem, a category and an offical answer."
+    "We use the [big_bench_audio](https://huggingface.co/datasets/ArtificialAnalysis/big_bench_audio) dataset that's hosted on Hugging Face. Big Bench Audio is an audio version of a subset of Big Bench Hard questions. The dataset can be used for evaluating the reasoning capabilities of models that support audio input. It contains an audio clip describing a logic problem, a category and an official answer."
    ]
   },
   {
@@ -206,7 +206,7 @@
    "outputs": [],
    "source": [
     "client = OpenAI(\n",
-    "    api_key=os.getenv(\"OPENAI_API_KEY_DISTILLATION\")\n",
+    "    api_key=os.getenv(\"OPENAI_API_KEY\")\n",
     ")"
    ]
   },
@@ -479,7 +479,7 @@
    "source": [
     "## Conclusion\n",
     "\n",
-    "In this cookbook, we covered a workflow for evaluating native audio inputs to a model using the OpenAI Evals API's. We demonstrated using a simple text grader to grade the text transcript of the audio response.\n",
+    "In this cookbook, we covered a workflow for evaluating native audio inputs to a model using the OpenAI Evals APIs. We demonstrated using a simple text grader to grade the text transcript of the audio response.\n",
     "### Next steps\n",
     "- Convert this example to your use case.  \n",
     "- Try using model based graders for additional flexibility in grading.\n",