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• 10 Key Google Analytics Limitations You Should Be Aware Of

  9 mai 2022, par Erin

  Google Analytics (GA) is the biggest player in the web analytics space. But is it as “universal” as its brand name suggests ?
  

  Over the years users have pointed out a number of major Google Analytics limitations. Many of these are even more visible in Google Analytics 4. 

  Introduced in 2020, Google Analytics 4 (GA4) has been sceptically received. As the sunset date of 1st, July 2023 for the current version, Google Universal Analytics (UA), approaches, the dismay grows stronger.

  To the point where people are pleading with others to intervene : 

  

  Main limitations of Google Analytics

  Google Analytics 4 is advertised as a more privacy-centred, comprehensive and “intelligent” web analytics platform. 

  According to Google, the newest version touts : 

  • Machine learning at its core provides better segmentation and fast-track access to granular insights 
  • Privacy-by-design controls, addressing restrictions on cookies and new regulatory demands 
  • More complete understanding of customer journeys across channels and devices 

  Some of these claims hold true. Others crumble upon a deeper investigation. Newly advertised Google Analytics capabilities such as ‘custom events’, ‘predictive insights’ and ‘privacy consent mode’ only have marginal improvements. 

  Complex setup, poor UI and lack of support with migration also leave many other users frustrated with GA4. 

  

  Let’s unpack all the current (and legacy) limitations of Google Analytics you should account for. 

  1. No Historical Data Imports 

  Google rushed users to migrate from Universal Analytics to Google Analytics 4. But they overlooked one important precondition — backwards compatibility. 

  You have no way to import data from Google Universal Analytics to Google Analytics 4. 

  Historical records are essential for analysing growth trends and creating benchmarks for new marketing campaigns. Effectively, you are cut short from past insights — and forced to start strategising from scratch. 

  At present, Google offers two feeble solutions : 

  • Run data collection in parallel and have separate reporting for GA4 and UA until the latter is shut down. Then your UA records are gone. 
  • For Ecommerce data, manually duplicate events from UA at a new GA4 property while trying to figure out the new event names and parameters. 

  Google’s new data collection model is the reason for migration difficulties. 

  In Google Analytics 4, all analytics hits types — page hits, social hits, app/screen view, etc. — are recorded as events. Respectively, the “‘event’ parameter in GA4 is different from one in Google Universal Analytics as the company explains : 

  

  This change makes migration tedious — and Google offers little assistance with proper events and custom dimensions set up. 

  2. Data Collection Limits 

  If you’ve wrapped your head around new GA4 events, congrats ! You did a great job, but the hassle isn’t over. 

  You still need to pay attention to new Google Analytics limits on data collection for event parameters and user properties. 

  

  These apply to :

  • Automatically collected events
  • Enhanced measurement events
  • Recommended events 
  • Custom events 

  When it comes to custom events, GA4 also has a limit of 25 custom parameters per event. Even though it seems a lot, it may not be enough for bigger websites. 

  You can get higher limits by upgrading to Google Analytics 360, but the costs are steep. 

  3. Limited GDPR Compliance 

  Google Analytics has a complex history with European GDPR compliance. 

  A 2020 ruling by the Court of Justice of the European Union (CJEU) invalidated the Privacy Shield framework Google leaned upon. This framework allowed the company to regulate EU-US data transfers of sensitive user data. 

  But after this loophole was closed, Google faced a heavy series of privacy-related fines :

  • French data protection authority, CNIL, ruled that  “the transfers to the US of personal data collected through Google Analytics are illegal” — and proceeded to fine Google for a record-setting €150 million at the beginning of 2022. 
  • Austrian regulators also deemed Google in breach of GDPR requirements and also branded the analytics as illegal. 

  Other EU-member states might soon proceed with similar rulings. These, in turn, can directly affect Google Analytics users, whose businesses could face brand damage and regulatory fines for non-compliance. In fact, companies cannot select where the collected analytics data will be stored — on European servers or abroad — nor can they obtain this information from Google.

  Getting a web analytics platform that allows you to keep data on your own servers or select specific Cloud locations is a great alternative. 

  Google also has been lax with its cookie consent policy and doesn’t properly inform consumers about data collection, storage or subsequent usage. Google Analytics 4 addresses this issue to an extent. 

  By default, GA4 relies on first-party cookies, instead of third-party ones — which is a step forward. But the user privacy controls are hard to configure without losing most of the GA4 functionality. Implementing user consent mode to different types of data collection also requires a heavy setup. 

  4. Strong Reliance on Sampled Data 

  To compensate for ditching third-party cookies, GA4 more heavily leans on sampled data and machine learning to fill the gaps in reporting. 

  In GA4 sampling automatically applies when you :

  • Perform advanced analysis such as cohort analysis, exploration, segment overlap or funnel analysis with not enough data 
  • Have over 10,000,000 data rows and generate any type of non-default report 

  Google also notes that data sampling can occur at lower thresholds when you are trying to get granular insights. If there’s not enough data or because Google thinks it’s too complex to retrieve. 

  In their words :

  

  Data sampling adds “guesswork” to your reports, meaning you can’t be 100% sure of data accuracy. The divergence from actual data depends on the size and quality of sampled data. Again, this isn’t something you can control. 

  Unlike Google Analytics 4, Matomo applies no data sampling. Your reports are always accurate and fully representative of actual user behaviours. 

  5. No Proper Data Anonymization 

  Data anonymization allows you to collect basic analytics about users — visits, clicks, page views — but without personally identifiable information (or PII) such as geo-location, assigns tracking ID or other cookie-based data. 

  This reduced your ability to :

  • Remarket 
  • Identify repeating visitors
  • Do advanced conversion attribution 

  But you still get basic data from users who ignored or declined consent to data collection. 

  By default, Google Analytics 4 anonymizes all user IP addresses — an upgrade from UA. However, it still assigned a unique user ID to each user. These count as personal data under GDPR. 

  For comparison, Matomo provides more advanced privacy controls. You can anonymize :

  • Previously tracked raw data 
  • Visitor IP addresses
  • Geo-location information
  • User IDs 

  This can ensure compliance, especially if you operate in a sensitive industry — and delight privacy-mindful users ! 

  6. No Roll-Up Reporting

  Getting a bird’s-eye view of all your data is helpful when you need hotkey access to main sites — global traffic volume, user count or percentage of returning visitors.

  With Roll-Up Reporting, you can see global-performance metrics for multiple localised properties (.co.nz, .co.uk, .com, etc,) in one screen. Then zoom in on specific localised sites when you need to. 

  

  7. Report Processing Latency 

  The average data processing latency is 24-48 hours with Google Analytics. 

  Accounts with over 200,000 daily sessions get data refreshes only once a day. So you won’t be seeing the latest data on core metrics. This can be a bummer during one-day promo events like Black Friday or Cyber Monday when real-time information can prove to be game-changing ! 

  Matomo processes data with lower latency even for high-traffic websites. Currently, we have 6-24 hour latency for cloud deployments. On-premises web analytics can be refreshed even faster — within an hour or instantly, depending on the traffic volumes. 

  8. No Native Conversion Optimisation Features

  Google Analytics users have to use third-party tools to get deeper insights like how people are interacting with your webpage or call-to-action.

  You can use the free Google Optimize tool, but it comes with limits : 

  • No segmentation is available 
  • Only 10 simultaneous running experiments allowed 

  There isn’t a native integration between Google Optimize and Google Analytics 4. Instead, you have to manually link an Optimize Container to an analytics account. Also, you can’t select experiment dimensions in Google Analytics reports.

  What’s more, Google Optimize is a basic CRO tool, best suited for split testing (A/B testing) of copy, visuals, URLs and page layouts. If you want to get more advanced data, you need to pay for extra tools. 

  Matomo comes with a native set of built-in conversion optimization features : 

  • Heatmaps 
  • User session recording 
  • Sales funnel analysis 
  • A/B testing 
  • Form submission analytics 

  

  9. Deprecated Annotations

  Annotations come in handy when you need to provide extra context to other team members. For example, point out unusual traffic spikes or highlight a leak in the sales funnel. 

  This feature was available in Universal Analytics but is now gone in Google Analytics 4. But you can still quickly capture, comment and share knowledge with your team in Matomo. 

  You can add annotations to any graph that shows statistics over time including visitor reports, funnel analysis charts or running A/B tests. 

  10. No White Label Option 

  This might be a minor limitation of Google Analytics, but a tangible one for agency owners. 

  Offering an on-brand, embedded web analytics platform can elevate your customer experience. But white label analytics were never a thing with Google Analytics, unlike Matomo. 

  Wrap Up 

  Google set a high bar for web analytics. But Google Analytics inherent limitations around privacy, reporting and deployment options prompt more users to consider Google Analytics alternatives, like Matomo. 

  With Matomo, you can easily migrate your historical data records and store customer data locally or in a designated cloud location. We operate by a 100% unsampled data principle and provide an array of privacy controls for advanced compliance. 

  Start your 21-day free trial (no credit card required) to see how Matomo compares to Google Analytics ! 

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• Google Optimize vs Matomo A/B Testing : Everything You Need to Know

  17 mars 2023, par Erin — Analytics Tips

  Google Optimize is a popular A/B testing tool marketers use to validate the performance of different marketing assets, website design elements and promotional offers. 

  But by September 2023, Google will sunset both free and paid versions of the Optimize product. 

  If you’re searching for an equally robust, but GDPR compliant, privacy-friendly alternative to Google Optimize, have a look at Matomo A/B Testing. 

  Integrated with our analytics platform and conversion rate optimisation (CRO) tools, Matomo allows you to run A/B and A/B/n tests without any usage caps or compromises in user privacy.

  Disclaimer : Please note that the information provided in this blog post is for general informational purposes only and is not intended to provide legal advice. Every situation is unique and requires a specific legal analysis. If you have any questions regarding the legal implications of any matter, please consult with your legal team or seek advice from a qualified legal professional.

  Google Optimize vs Matomo : Key Capabilities Compared 

  This guide shows how Matomo A/B testing stacks against Google Optimize in terms of features, reporting, integrations and pricing.

  Supported Platforms 

  Google Optimize supports experiments for dynamic websites and single-page mobile apps only. 

  If you want to run split tests in mobile apps, you’ll have to do so via Firebase — Google’s app development platform. It also has a free tier but paid usage-based subscription kicks in after your product(s) reaches a certain usage threshold. 

  Google Optimize also doesn’t support CRO experiments for web or desktop applications, email campaigns or paid ad campaigns.Matomo A/B Testing, in contrast, allows you to run experiments in virtually every channel. We have three installation options — using JavaScript, server-side technology, or our mobile tracking SDK. These allow you to run split tests in any type of web or mobile app (including games), a desktop product, or on your website. Also, you can do different email marketing tests (e.g., compare subject line variants).

  A/B Testing 

  A/B testing (split testing) is the core feature of both products. Marketers use A/B testing to determine which creative elements such as website microcopy, button placements and banner versions, resonate better with target audiences. 

  You can benchmark different versions against one another to determine which variation resonates more with users. Or you can test an A version against B, C, D and beyond. This is called A/B/n testing. 

  Both Matomo A/B testing and Google Optimize let you test either separate page elements or two completely different landing page designs, using redirect tests. You can show different variants to different user groups (aka apply targeting criteria). For example, activate tests only for certain device types, locations or types of on-site behaviour. 

  The advantage of Matomo is that we don’t limit the number of concurrent experiments you can run. With Google Optimize, you’re limited to 5 simultaneous experiments. Likewise, 

  Matomo lets you select an unlimited number of experiment objectives, whereas Google caps the maximum choice to 3 predefined options per experiment. 

  Objectives are criteria the underlying statistical model will use to determine the best-performing version. Typically, marketers use metrics such as page views, session duration, bounce rate or generated revenue as conversion goals. 

  

  Multivariate testing (MVT)

  Multivariate testing (MVT) allows you to “pack” several A/B tests into one active experiment. In other words : You create a stack of variants to determine which combination drives the best marketing outcomes. 

  For example, an MVT experiment can include five versions of a web page, where each has a different slogan, product image, call-to-action, etc. Visitors are then served with a different variation. The tracking code collects data on their behaviours and desired outcomes (objectives) and reports the results.

  MVT saves marketers time as it’s a great alternative to doing separate A/B tests for each variable. Both Matomo and Google Optimize support this feature. However, Google Optimize caps the number of possible combinations at 16, whereas Matomo has no limits. 

  Redirect Tests

  Redirect tests, also known as split URL tests, allow you to serve two entirely different web page versions to users and compare their performance. This option comes in handy when you’re redesigning your website or want to test a localised page version in a new market. 

  Also, redirect tests are a great way to validate the performance of bottom-of-the-funnel (BoFU) pages as a checkout page (for eCommerce websites), a pricing page (for SaaS apps) or a contact/booking form (for a B2B service businesses). 

  You can do split URL tests with Google Optimize and Matomo A/B Testing. 

  Experiment Design 

  Google Optimize provides a visual editor for making simple page changes to your website (e.g., changing button colour or adding several headline variations). You can then preview the changes before publishing an experiment. For more complex experiments (e.g., testing different page block sequences), you’ll have to codify experiments using custom JavaScript, HTML and CSS.

  In Matomo, all A/B tests are configured on the server-side (i.e., by editing your website’s raw HTML) or client-side via JavaScript. Afterwards, you use the Matomo interface to start or schedule an experiment, set objectives and view reports. 

  Experiment Configuration 

  Marketers know how complex customer journeys can be. Multiple factors — from location and device to time of the day and discount size — can impact your conversion rates. That’s why a great CRO app allows you to configure multiple tracking conditions. 

  Matomo A/B testing comes with granular controls. First of all, you can decide which percentage of total web visitors participate in any given experiment. By default, the number is set to 100%, but you can change it to any other option. 

  Likewise, you can change which percentage of traffic each variant gets in an experiment. For example, your original version can get 30% of traffic, while options A and B receive 40% each. We also allow users to specify custom parameters for experiment participation. You can only show your variants to people in specific geo-location or returning visitors only. 

  Finally, you can select any type of meaningful objective to evaluate each variant’s performance. With Matomo, you can either use standard website analytics metrics (e.g., total page views, bounce rate, CTR, visit direction, etc) or custom goals (e.g., form click, asset download, eCommerce order, etc). 

  In other words : You’re in charge of deciding on your campaign targeting criteria, duration and evaluation objectives.

  A free Google Optimize account comes with three main types of user targeting options : 

  • Geo-targeting at city, region, metro and country levels. 

  • Technology targeting  by browser, OS or device type, first-party cookie, etc. 
  • Behavioural targeting based on metrics like “time since first arrival” and “page referrer” (referral traffic source). 

  Users can also configure other types of tracking scenarios (for example to only serve tests to signed-in users), using condition-based rules. 

  Reporting 

  Both Matomo and Google Optimize use different statistical models to evaluate which variation performs best. 

  Matomo relies on statistical hypothesis testing, which we use to count unique visitors and report on conversion rates. We analyse all user data (with no data sampling applied), meaning you get accurate reporting, based on first-hand data, rather than deductions. For that reason, we ask users to avoid drawing conclusions before their experiment participation numbers reach a statistically significant result. Typically, we recommend running an experiment for at least several business cycles to get a comprehensive report. 

  Google Optimize, in turn, uses Bayesian inference — a statistical method, which relies on a random sample of users to compare the performance rates of each creative against one another. While a Bayesian model generates CRO reports faster and at a bigger scale, it’s based on inferences.

  Model developers need to have the necessary skills to translate subjective prior beliefs about the probability of a certain event into a mathematical formula. Since Google Optimize is a proprietary tool, you cannot audit the underlying model design and verify its accuracy. In other words, you trust that it was created with the right judgement. 

  In comparison, Matomo started as an open-source project, and our source code can be audited independently by anyone at any time. 

  Another reporting difference to mind is the reporting delays. Matomo Cloud generates A/B reports within 6 hours and in only 1 hour for Matomo On-Premise. Google Optimize, in turn, requires 12 hours from the first experiment setup to start reporting on results. 

  When you configure a test experiment and want to quickly verify that everything is set up correctly, this can be an inconvenience.

  User Privacy & GDPR Compliance 

  Google Optimize works in conjunction with Google Analytics, which isn’t GDPR compliant. 

  For all website traffic from the EU, you’re therefore obliged to show a cookie consent banner. The kicker, however, is that you can only show an Optimize experiment after the user gives consent to tracking. If the user doesn’t, they will only see an original page version. Considering that almost 40% of global consumers reject cookie consent banners, this can significantly affect your results.

  This renders Google Optimize mostly useless in the EU since it would only allow you to run tests with a fraction ( 60%) of EU traffic — and even less if you apply any extra targeting criteria. 

  In comparison, Matomo is fully GDPR compliant. Therefore, our users are legally exempt from displaying cookie-consent banners in most EU markets (with Germany and the UK being an exception). Since Matomo A/B testing is part of Matomo web analytics, you don’t have to worry about GDPR compliance or breaches in user privacy. 

  Digital Experience Intelligence 

  You can get comprehensive statistical data on variants’ performance with Google Optimize. But you don’t get further insights on why some tests are more successful than others. 

  Matomo enables you to collect more insights with two extra features :

  • User session recordings : Monitor how users behave on different page versions. Observe clicks, mouse movements, scrolls, page changes, and form interactions to better understand the users’ cumulative digital experience. 
  • Heatmaps : Determine which elements attract the most users’ attention to fine-tune your split tests. With a standard CRO tool, you only assume that a certain page element does matter for most users. A heatmap can help you determine for sure. 

  Both of these features are bundled into your Matomo Cloud subscription. 

  Integrations 

  Both Matomo and Google Optimize integrate with multiple other tools. 

  Google Optimize has native integrations with other products in the marketing family — GA, Google Ads, Google Tag Manager, Google BigQuery, Accelerated Mobile Pages (AMP), and Firebase. Separately, other popular marketing apps have created custom connectors for integrating Google Optimize data. 

  Matomo A/B Testing, in turn, can be combined with other web analytics and CRO features such as Funnels, Multi-Channel Attribution, Tag Manager, Form Analytics, Heatmaps, Session Recording, and more ! 

  You can also conveniently export your website analytics or CRO data using Matomo Analytics API to analyse it in another app. 

  Pricing 

  Google Optimize is a free tool but has usage caps. If you want to schedule more than 5 concurrent experiments or test more than 16 variants at once, you’ll have to upgrade to Optimize 360. Optimize 360 prices aren’t listed publicly but are said to be closer to six figures per year. 

  Matomo A/B Testing is available with every Cloud subscription (starting from €19) and Matomo On-Premise users can also get A/B Testing as a plugin (starting from €199/year). In each case, there are no caps or data limits. 

  Google Optimize vs Matomo A/B Testing : Comparison Table

  Features/capabilities	Google Optimize	Matomo A/B test
  Supported channels	Web	Web, mobile, email, digital campaigns
  A/B testing
  Multivariate testing (MVT)
  Split URL tests
  Web analytics integration	Native with UA/GA4	Native with Matomo You can also migrate historical UA (GA3) data to Matomo
  Audience segmentation	Basic	Advanced
  Geo-targeting
  Technology targeting
  Behavioural targeting	Basic	Advanced
  Reporting model	Bayesian analysis	Statistical hypothesis testing
  Report availability	Within 12 hours after setup	6 hours for Matomo Cloud 1 hour for Matomo On-Premise
  Heatmaps		Included with Matomo Cloud
  Session recordings		Included with Matomo Cloud
  GDPR compliance
  Support	Self-help desk on a free tier	Self-help guides, user forum, email
  Price	Free limited tier	From €19 for Cloud subscription From €199/year as plugin for On-Premise

  Final Thoughts : Who Benefits the Most From an A/B Testing Tool ?

  Split testing is an excellent method for validating various assumptions about your target customers. 

  With A/B testing tools you get a data-backed answer to research hypotheses such as “How different pricing affects purchases ?”, “What contact button placement generates more clicks ?”, “Which registration form performs best with new app subscribers ?” and more. 

  Such insights can be game-changing when you’re trying to improve your demand-generation efforts or conversion rates at the BoFu stage. But to get meaningful results from CRO tests, you need to select measurable, representative objectives.

  For example, split testing different pricing strategies for low-priced, frequently purchased products makes sense as you can run an experiment for a couple of weeks to get a statistically relevant sample. 

  But if you’re in a B2B SaaS product, where the average sales cycle takes weeks (or months) to finalise and things like “time-sensitive discounts” or “one-time promos” don’t really work, getting adequate CRO data will be harder. 

  To see tangible results from CRO, you’ll need to spend more time on test ideation than implementation. Your team needs to figure out : which elements to test, in what order, and why. 

  Effective CRO tests are designed for a specific part of the funnel and assume that you’re capable of effectively identifying and tracking conversions (goals) at the selected stage. This alone can be a complex task since not all customer journeys are alike. For SaaS websites, using a goal like “free trial account registration” can be a good starting point.

  A good test also produces a meaningful difference between the proposed variant and the original version. As Nima Yassini, Partner at Deloitte Digital, rightfully argues :

  “I see people experimenting with the goal of creating an uplift. There’s nothing wrong with that, but if you’re only looking to get wins you will be crushed when the first few tests fail. The industry average says that only one in five to seven tests win, so you need to be prepared to lose most of the time”.

  In many cases, CRO tests don’t provide the data you expected (e.g., people equally click the blue and green buttons). In this case, you need to start building your hypothesis from scratch. 

  At the same time, it’s easy to get caught up in optimising for “vanity metrics” — such that look good in the report, but don’t quite match your marketing objectives. For example, better email headline variations can improve your email open rates. But if users don’t proceed to engage with the email content (e.g. click-through to your website or use a provided discount code), your efforts are still falling short. 

  That’s why developing a baseline strategy is important before committing to an A/B testing tool. Google Optimize appealed to many users because it’s free and allows you to test your split test strategy cost-effectively. 

  With its upcoming depreciation, many marketers are very committed to a more expensive A/B tool (especially when they’re not fully sure about their CRO strategy and its results). 

  Matomo A/B testing is a cost-effective, GDPR-compliant alternative to Google Optimize with a low learning curve and extra competitive features. 

  Discover if Matomo A/B Testing is the ideal Google Optimize alternative for your organization with our free 21-day trial. No credit card required.

• Google Speech Recognition API output errors, unsure why they're occuring

  8 novembre 2019, par Requiem_7

  This is the output for when I feed flac files into Google’s Speech Recognition API. It says that if starts and finishes most of the files but then it gives me these errors when it nears the end. I have checked and all these files are native flac files. I took out a good chunk of the output above "source/out70.flac started" becuase it’s all the same besides the file number.

  source/out70.flac started
  
  source/out25.flac started
  
  source/out17.flac done
  
  source/out18.flac started
  
  source/out25.flac done
  
  source/out20.flac done
  
  source/out21.flac started
  
  source/out10.flac done
  
  source/out100.flac started
  
  source/out14.flac done
  
  source/out18.flac done
  
  source/out21.flac done
  
  Traceback (most recent call last):
  
    File "C:\Users\hmkur\AppData\Roaming\Python\Python37\site-packages\speech_recognition\__init__.py", line 203, in __enter__
  
      self.audio_reader = wave.open(self.filename_or_fileobject, "rb")
  
    File "C:\Program Files (x86)\Python37-32\lib\wave.py", line 510, in open
  
      return Wave_read(f)
  
    File "C:\Program Files (x86)\Python37-32\lib\wave.py", line 164, in __init__
  
      self.initfp(f)
  
    File "C:\Program Files (x86)\Python37-32\lib\wave.py", line 129, in initfp
  
      self._file = Chunk(file, bigendian = 0)
  
    File "C:\Program Files (x86)\Python37-32\lib\chunk.py", line 63, in __init__
  
      raise EOFError
  
  EOFError
  
  
  
  During handling of the above exception, another exception occurred:
  
  
  
  Traceback (most recent call last):
  
    File "C:\Users\hmkur\AppData\Roaming\Python\Python37\site-packages\speech_recognition\__init__.py", line 208, in __enter__
  
      self.audio_reader = aifc.open(self.filename_or_fileobject, "rb")
  
    File "C:\Program Files (x86)\Python37-32\lib\aifc.py", line 917, in open
  
      return Aifc_read(f)
  
    File "C:\Program Files (x86)\Python37-32\lib\aifc.py", line 352, in __init__
  
      self.initfp(file_object)
  
    File "C:\Program Files (x86)\Python37-32\lib\aifc.py", line 314, in initfp
  
      chunk = Chunk(file)
  
    File "C:\Program Files (x86)\Python37-32\lib\chunk.py", line 63, in __init__
  
      raise EOFError
  
  EOFError
  
  
  
  During handling of the above exception, another exception occurred:
  
  
  
  Traceback (most recent call last):
  
    File "C:\Users\hmkur\AppData\Roaming\Python\Python37\site-packages\speech_recognition\__init__.py", line 234, in __enter__
  
      self.audio_reader = aifc.open(aiff_file, "rb")
  
    File "C:\Program Files (x86)\Python37-32\lib\aifc.py", line 917, in open
  
      return Aifc_read(f)
  
    File "C:\Program Files (x86)\Python37-32\lib\aifc.py", line 358, in __init__
  
      self.initfp(f)
  
    File "C:\Program Files (x86)\Python37-32\lib\aifc.py", line 314, in initfp
  
      chunk = Chunk(file)
  
    File "C:\Program Files (x86)\Python37-32\lib\chunk.py", line 63, in __init__
  
      raise EOFError
  
  EOFError
  
  
  
  During handling of the above exception, another exception occurred:
  
  
  
  Traceback (most recent call last):
  
    File "C:\Users\hmkur\Desktop\Python\Transcribing_Audio_GoogleAPI_Python\fast.py", line 92, in <module>
  
      all_text = pool.map(transcribe, enumerate(files))
  
    File "C:\Program Files (x86)\Python37-32\lib\multiprocessing\pool.py", line 268, in map
  
      return self._map_async(func, iterable, mapstar, chunksize).get()
  
    File "C:\Program Files (x86)\Python37-32\lib\multiprocessing\pool.py", line 657, in get
  
      raise self._value
  
    File "C:\Program Files (x86)\Python37-32\lib\multiprocessing\pool.py", line 121, in worker
  
      result = (True, func(*args, **kwds))
  
    File "C:\Program Files (x86)\Python37-32\lib\multiprocessing\pool.py", line 44, in mapstar
  
      return list(map(*args))
  
    File "C:\Users\hmkur\Desktop\Python\Transcribing_Audio_GoogleAPI_Python\fast.py", line 82, in transcribe
  
      with sr.AudioFile(name) as source:
  
    File "C:\Users\hmkur\AppData\Roaming\Python\Python37\site-packages\speech_recognition\__init__.py", line 236, in __enter__
  
      raise ValueError("Audio file could not be read as PCM WAV, AIFF/AIFF-C, or Native FLAC; check if file is corrupted or in another format")
  
  ValueError: Audio file could not be read as PCM WAV, AIFF/AIFF-C, or Native FLAC; check if file is corrupted or in another format
  
  </module>