Deepgram Streaming Transcription Accuracy Issue With Interim_results True

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Hey guys, let's dive into a tricky issue we've been facing with Deepgram's streaming transcription when using the interim_results=True setting. We've noticed a significant drop in accuracy compared to when interim_results is set to False. This is a big deal for us because we need reliable interim transcripts for features like interrupt handling in our real-time voice pipeline. So, let's break down the problem, the steps we've taken to reproduce it, and what we're hoping to see as the expected behavior.

What's the Deal with the Current Behavior?

So, here's the main problem: when we enable interim_results=True in our Deepgram streaming transcription configuration, the transcription accuracy takes a nosedive. Specifically, the interim transcripts we're getting are proving to be way less reliable than the final transcripts. They often stray pretty far from what's actually being said. This makes it super challenging to maintain the level of accuracy we need while also trying to support interrupt handling – a feature that's crucial for a smooth user experience. To really get to the bottom of this, we put things to the test using recorded audio clips that mimic the kind of utterances we encounter in our production environment. I've even attached the audio clips we used, along with the actual and expected results, so you can see exactly what we're dealing with. The final transcription for interim_results=True was created using Deepgram's official guide for Using Interim Results, so we're following best practices here. Let's look at some specific examples to illustrate the issue.

Example Utterances and Transcription Results

We tested with several audio clips, and here's a breakdown of the expected and actual transcriptions we got with both interim_results=False and interim_results=True:

  1. coffee options

    • Expected: "Hello, I would like to order a coffee. Can you please give me all the coffee options?"
    • Actual (interim_results=False): "hello i would like to order a coffee can you please give me all the coffee options"
    • Actual (interim_results=True): "Hello, I would like to order a coffee. Can you please give me all the coffee option?"

  2. ask sandwiches

    • Expected: "Do you have any sandwiches available?"
    • Actual (interim_results=False): "do you have any sandwiches available"
    • Actual (interim_results=True): "Do you have any sandwiches available"

  3. list sandwich options

    • Expected: "Can you list down all the available sandwiches?"
    • Actual (interim_results=False): "can you list down all the available salaries"
    • Actual (interim_results=True): "Can you list all the available"

  4. grilled chicken sandwich

    • Expected: "I would like to order a grilled chicken sandwich. Please add that to the order."
    • Actual (interim_results=False): "i would like to order a great chicken sandwich please add that to the order"
    • Actual (interim_results=True): "I would like to order a grilled check sandwich. Please add to the order."

  5. reservation

    • Expected: "I would like to make a reservation for tomorrow, seven people 4 PM."
    • Actual (interim_results=False): "i would like to make a reservation for tomorrow telling people four pm"
    • Actual (interim_results=True): "I would like to make a reserving for tomorrow. Seven people PM."

  6. reservation name

    • Expected: "Please make the reservation under the name Sonali, thank you."
    • Actual (interim_results=False): "please make the reservation under the name सोनाली thank you"
    • Actual (interim_results=True): "Please make the reservation under the name for Thank you."

As you can see, in several instances, the transcriptions with interim_results=True are noticeably less accurate than those with interim_results=False. Words are missed, phrases are misinterpreted, and the overall coherence suffers. This inconsistency makes it difficult to rely on the interim results for real-time decision-making.

You can grab the audio clips we used for testing right here: ask_sandwich,coffee options,grilled_chicken_sandwich.zip. Feel free to run your own tests and see if you can reproduce the issue.

Steps to Reproduce the Issue

Okay, so how can you reproduce this transcription accuracy degradation issue yourself? It's pretty straightforward. We've been testing with live streaming transcription, and here's the process:

  1. First, run a live transcription with interim_results=False. Make sure your other options are set up correctly for your use case (we'll share our configuration details later). When you do this, you should observe a relatively high level of accuracy in the final transcript.
  2. Next, run the exact same audio with interim_results=True. Keep all other options the same as in step one. This is where you should see the accuracy drop. The interim transcripts, in particular, will likely be less reliable and diverge more from the final, intended transcript.

By following these steps, you should be able to observe the same behavior we've been seeing – a noticeable degradation in transcription accuracy when interim_results is enabled. This consistent behavior across tests indicates that the issue isn't just a fluke but rather a systematic problem with how interim results are being handled.

Delving Deeper into the Configuration

To provide more context, let's talk about the specific configurations we're using. This will help you understand the parameters we're working with and potentially identify any configuration-related factors that might be contributing to the issue. In our initial setup, where we were getting good transcription accuracy, we used the following LiveOptions:

LiveOptions(
    model="nova-3",
    encoding='linear16',
    sample_rate=8000,
    language="multi",
    interim_results=False,
    endpointing=300,
    filler_words=True,
)

This configuration gave us pretty solid results for our needs. However, to enable interrupt handling, we needed to make some changes. This led us to update our configuration to include interim_results=True and other related settings:

LiveOptions(
    model="nova-3",
    encoding='linear16',
    sample_rate=8000,
    language="multi",
    interim_results=True,
    endpointing=300,
    filler_words=True,
    vad_events=True,
    utterance_end_ms=1000,
    punctuate=True,
)

The key difference here is the switch to interim_results=True and the addition of vad_events, utterance_end_ms, and punctuate. These additions were specifically aimed at improving interrupt handling and the overall user experience. However, as we've seen, this change introduced the accuracy degradation issue we're discussing. It's worth noting that we're using the "nova-3" model, which is one of Deepgram's more advanced models, so we wouldn't expect the model itself to be the primary source of the problem. The encoding and sample rate are also standard settings. The endpointing and filler_words options are used to control how the transcription engine handles pauses and filler words, respectively, but they shouldn't directly impact the core accuracy of the transcription.

What We're Hoping to See: Expected Behavior

Alright, so what are we expecting here? What would be the ideal behavior when using interim_results=True? Well, first and foremost, interim results should ideally be reasonably close to the final transcripts. This doesn't mean they need to be perfect, but they should accurately reflect the gist of what's being said. If the interim results are wildly inaccurate, they become essentially useless for applications like interrupt handling, where you need to make decisions based on the user's current utterance.

Secondly, and perhaps more importantly, accuracy should not drop significantly when enabling interim_results=True. We understand that there might be some minor differences between interim and final transcripts, but the core accuracy should remain consistent. Enabling a feature like interim results shouldn't come at the cost of overall transcription quality. We're aiming for a solution where we can have the best of both worlds: reliable interim results for real-time processing and high-quality final transcripts. The current behavior, where accuracy degrades noticeably, forces us to choose between these two crucial aspects, which isn't ideal.

Our Environment Setup

To give you the full picture, let's talk about our environment setup. This information can be crucial for troubleshooting and identifying potential environment-specific factors that might be at play. Here's the key information about our setup:

  • Operating System/Version: MacOS 15.5
  • Python Version: 3.12.8

We're running our tests on a MacOS environment, which is a common development platform. We're also using a relatively recent version of Python, which should be compatible with the Deepgram Python SDK. This information helps to narrow down the potential causes of the issue. If we were using a different operating system or an older version of Python, that might point to compatibility issues or bugs in the SDK related to specific environments. However, given our setup, it's less likely that the environment itself is the primary culprit. It's more likely that the issue lies within the Deepgram SDK or the interaction between the interim_results setting and other configuration options.

Context: Our Real-Time Voice Pipeline

So, where does this issue fit into the bigger picture? We're using Deepgram for speech-to-text (STT) in our real-time voice pipeline. This pipeline is a core component of our application, handling the conversion of spoken audio into text for various downstream processes. Accurate and reliable transcription is absolutely essential for the pipeline to function effectively. If the transcriptions are inaccurate, it can lead to errors in downstream tasks, degrade the user experience, and ultimately impact the overall performance of our application. That's why this transcription accuracy degradation issue is such a high priority for us. We need to ensure that our voice pipeline is as robust and accurate as possible. The use of Deepgram was intended to provide that level of reliability, but the current behavior with interim_results=True is preventing us from achieving our goals. We're actively exploring potential solutions and working to understand the root cause of the issue so that we can restore the high level of accuracy we were seeing previously.

Next Steps and Potential Solutions

So, where do we go from here? We're actively investigating potential solutions and working to understand the root cause of this transcription accuracy degradation. Here are some of the avenues we're exploring:

  1. Experimenting with different Deepgram models: While we're currently using the "nova-3" model, we might try testing with other models to see if the issue is specific to that model or more general.
  2. Adjusting other configuration options: We'll be experimenting with different combinations of settings, such as endpointing, vad_events, and utterance_end_ms, to see if any of these are interacting negatively with interim_results.
  3. Analyzing the raw audio data: We'll be examining the audio clips more closely to see if there are any characteristics of the audio itself that might be contributing to the problem.
  4. Consulting with Deepgram support: We'll be reaching out to Deepgram support to get their insights and guidance on this issue. They may have encountered similar problems before or be able to suggest specific solutions.
  5. Exploring alternative approaches to interrupt handling: If we can't resolve the accuracy issue with interim_results=True, we may need to consider alternative methods for implementing interrupt handling in our voice pipeline.

We're committed to finding a solution that allows us to maintain high transcription accuracy while also supporting the features we need for a smooth and responsive user experience. We'll keep you updated on our progress as we continue to investigate this issue.