+
+### Step 2: Select the Speech to Text provider
+
+* Select the language in "Speech-to-Text Provider" from the dropdown provided.
+
+{% hint style="info" %}
+Use the "Filter by Language" dropdown, if you are unsure which models will work with your source language
+{% endhint %}
+
+
+
+### Step 3: Select your translation model
+
+* Click on the "Translate" checkbox
+* Select the translation model of your choice
+
+### Step 4: Hit "Run"
+
+* Click on the "Run" button
+
+### FAQS
+
+Q: How do I test the ASR models that can transcribe Swahili?
+
+A: Use the "Filter by Language" dropdown, if you are unsure which models will work with your source language.
+
+Q: In the translate section I can see "Google Translate" and "GhanaNLP", which model should I use?
+
+A: If you are translating an African Language you can test if GhanaNLP is a better choice. GhanaNLP Machine Translation supports: Twi, Ewe, Ga, Fanti, Yoruba, Dagbani, Kikuyu, Fra fra, Luo (Kenya, Tanzania), Meru, Kusaal
+
+Q: I have tested a few models, but I want to evaluate a larger dataset without using the API, is that possible?
+
+A: Yes! It is very easy to set up large-scale ASR evaluations in Gooey! Here is the guide for:
+
+
+
+
+
+Q: What is the "Prompt" section when I choose GPT4o-Audio?
+
+A: GPT4o-Audio is an LLM based transcription model, the prompt section will allow you to output the transcribed audio in more specific ways. For example, if you input a Hindi audio sample, you can say "Translate the Hindi recording as accurately as possible". This will use the LLM directly to make the translation. You could also use it in other innovative ways like "Summarize the Hindi recording to English in bullet points" which could give you the salient points of the recording directly. Like the example here:
+
+{% embed url="https://gooey.ai/speech/?run_id=1rnoo9r71o3q&uid=fm165fOmucZlpa5YHupPBdcvDR02" %}
+
diff --git a/guides/how-to-create-language-evaluation-for-asr.md b/guides/how-to-use-asr/how-to-create-language-evaluation-for-asr.md
similarity index 65%
rename from guides/how-to-create-language-evaluation-for-asr.md
rename to guides/how-to-use-asr/how-to-create-language-evaluation-for-asr.md
index f48324b..126001a 100644
--- a/guides/how-to-create-language-evaluation-for-asr.md
+++ b/guides/how-to-use-asr/how-to-create-language-evaluation-for-asr.md
@@ -28,7 +28,7 @@ There are several components to test:
#### Also see:
-
## Getting Started
@@ -39,7 +39,7 @@ There are several components to test:
3. Add the human-created transcription for each sample
4. Add the English translation for each sample
-
+
### Step 1 - Select the ASR Models
@@ -49,37 +49,37 @@ Head to our bulk and eval workflow.
In the example, we have already pre-filled the various models that can be tested. You can choose the ones you want to run by selecting it in the dropdown.
-
+
### Step 2 - Add your CSV/Google Sheets
Upload your CSV/Google Sheet from [Step 0](how-to-create-language-evaluation-for-asr.md#step-0-prepare-your-data). In this example, we have used a Google Sheet of 10 Audio Samples with transcripts and translations. A preview of your sheet will appear once it is correctly uploaded.
-
+
### Step 3 - Select the input column
Select the column in the input from the dropdown box. The outputs will appear as various columns. In this example, it will be the "audios" column
-
+
### Step 4 - Select the pre-built evaluator
In the "Evaluation Workflows" section select the "Speech Recognition Model Evaluator".
-
+
### Step 5 - Hit Submit
Once you hit submit, the selected ASR model workflows (see [Step 1](how-to-create-language-evaluation-for-asr.md#step-1-select-the-asr-models)) will run for each audio file in the sheet (see [Step 2](how-to-create-language-evaluation-for-asr.md#step-2-add-your-csv-google-sheets)). An output CSV will be generated on the right-hand side of the page.
-
+
After the runs are complete, the selected Evaluator (see [Step 4](how-to-create-language-evaluation-for-asr.md#step-4-select-the-pre-built-evaluator)), will compare the ASR model outputs to the human-generated translations. It will assess and rate how accurately each model has translated the audio sample.
A bar graph with the performance will appear once the entire evaluation is complete.
-
+
@@ -89,7 +89,7 @@ A bar graph with the performance will appear once the entire evaluation is compl
A: Arrange the audio sample link in the first column, for each audio link add transcriptions and translations in the respective row.
-
+
#### Q: What is the ideal length of the recording?