Speech to Text is not showing for the complete audio file

[sukumarpm]

Hi There,

I have an audio file(Language: Malayalam, India). This audio file has drama with more than one speaker(not at same time). when i try to Transcribe(v1/v2), only few dialogues are getting transcribed. I need to transcribe content of entire audio file. my file is with a length of 60 seconds.(short) can anyone please assist?.

[nceniza]

It is possible that the file has a quality issues, I recommend following this documentation for best practices when sending files to the API( factors my vary from file types to encodings etc etc) 

[sukumarpm]

Thanks. But, The words which are already transcribed have the same quality of other words which are not being transcribed. is diarization(multi speakers) not incorporated for malayalam language in Google speech to Text service?

[nceniza]

It is incorporated but as of the moment is in preview stage: (which currently has a limited support ) Can you try following these steps found here for enabling speaker diarization? 

[sukumarpm]

I tried to enable Speaker Diarization. I am able to see only "speaker_tag:1". When i refer with the actual audio file, words from other speakers are missing. As per the documentation here , i have changed only the file name, AudioEncoding.MP3 and language_code="ml-IN" only. Please see below the response. Kindly let me know if you need my audio which i am testing.

response: results {
alternatives {
transcript: "\340\264\241\340\265\215\340\264\260\340\265\210\340\264\265\340\265\274 \340\264\265\340\264\243\340\265\215\340\264\237\340\264\277 \340\264\250\340\264\277\340\265\274\340\264\244\340\265\215\340\264\244\340\264\277"
confidence: 0.8085001
words {
start_time {
seconds: 2
}
end_time {
seconds: 4
nanos: 300000000
}
word: "\340\264\241\340\265\215\340\264\260\340\265\210\340\264\265\340\265\274"
}
words {
start_time {
seconds: 4
nanos: 300000000
}
end_time {
seconds: 4
nanos: 600000000
}
word: "\340\264\265\340\264\243\340\265\215\340\264\237\340\264\277"
}
words {
start_time {
seconds: 4
nanos: 600000000
}
end_time {
seconds: 4
nanos: 800000000
}
word: "\340\264\250\340\264\277\340\265\274\340\264\244\340\265\215\340\264\244\340\264\277"
}
}
result_end_time {
seconds: 10
nanos: 200000000
}
language_code: "ml-in"
}
results {
alternatives {
transcript: " \340\264\250\340\264\277\340\264\231\340\265\215\340\264\231\340\265\276 \340\264\206\340\264\260\340\264\276\340\264\247\340\264\225\340\264\260\340\265\206 \340\264\265\340\264\260\340\265\201\340\264\250\340\265\215\340\264\250\340\264\244\340\265\215 \340\264\236\340\264\231\340\265\215\340\264\231\340\265\276 \340\264\254\340\265\213\340\264\202\340\264\254\340\265\206\340\264\257\340\264\277\340\264\262\340\264\276\340\264\257\340\264\277\340\264\260\340\265\201\340\264\250\340\265\215\340\264\250\340\265\201 \340\264\225\340\265\215\340\264\225\340\265\201\340\264\263\340\265\215\340\264\263 \340\264\265\340\264\264\340\264\277 \340\264\222\340\264\250\340\265\215\340\264\250\340\265\201 \340\264\252\340\264\261\340\264\236\340\265\215\340\264\236\340\265\201\340\264\244\340\264\260\340\265\201\340\264\256\340\265\213 \340\264\271\340\264\262\340\265\213 \340\264\222\340\264\250\340\265\215\340\264\250\340\265\215 \340\264\232\340\265\213\340\264\246\340\264\277\340\264\232\340\265\215\340\264\232\340\265\213\340\264\237\340\265\215\340\264\237\340\265\206 \340\264\265\340\264\264\340\264\277 \340\264\222\340\264\250\340\265\215\340\264\250\340\265\215 \340\264\252\340\264\261\340\264\236\340\265\215\340\264\236\340\265\201 \340\264\244\340\264\260\340\264\276\340\264\256\340\265\213 \340\264\225\340\265\212\340\264\237\340\265\215\340\264\237\340\264\276\340\264\260\340\264\244\340\265\215\340\264\244\340\264\277\340\264\262\340\265\207\340\264\225\340\265\215\340\264\225\340\265\215 \340\264\256\340\264\261\340\265\201\340\264\252\340\264\237\340\264\277 \340\264\225\340\264\277\340\264\237\340\265\215\340\264\237\340\265\201\340\264\250\340\265\215\340\264\250\340\264\277\340\264\262\340\265\215\340\264\262\340\264\262\340\265\215\340\264\262\340\265\213 \340\264\250\340\264\277\340\264\231\340\265\215\340\264\231\340\265\276\340\264\225\340\265\215\340\264\225\340\265\215 \340\264\216\340\264\231\340\265\215\340\264\225\340\264\277\340\264\262\340\265\201\340\264\202 \340\264\265\340\264\264\340\264\277 \340\264\205\340\264\261\340\264\277\340\264\257\340\264\276\340\264\256\340\265\213 \340\264\205\340\264\261\340\264\277\340\264\257\340\264\244\340\265\215\340\264\244\340\264\277\340\264\262\340\265\215\340\264\262 \340\264\265\340\264\264\340\264\277 \340\264\205\340\264\261\340\264\277\340\264\257\340\264\276\340\264\250\340\265\201\340\264\263\340\265\215\340\264\263 \340\264\257\340\264\234\340\265\215\340\264\236\340\264\202"
confidence: 0.7363452
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nanos: 600000000
}
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}
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nanos: 600000000
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nanos: 100000000
}
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}
words {
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nanos: 100000000
}
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nanos: 700000000
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}
words {
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nanos: 700000000
}
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nanos: 200000000
}
word: "\340\264\236\340\264\231\340\265\215\340\264\231\340\265\276"
}
words {
start_time {
seconds: 15
nanos: 200000000
}
end_time {
seconds: 15
nanos: 400000000
}
word: "\340\264\254\340\265\213\340\264\202\340\264\254\340\265\206\340\264\257\340\264\277\340\264\262\340\264\276\340\264\257\340\264\277\340\264\260\340\265\201\340\264\250\340\265\215\340\264\250\340\265\201"
}
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}
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nanos: 100000000
}
word: "\340\264\265\340\264\264\340\264\277"
}
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nanos: 200000000
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nanos: 200000000
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nanos: 400000000
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}
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nanos: 400000000
}
end_time {
seconds: 30
nanos: 500000000
}
word: "\340\264\271\340\264\262\340\265\213"
}
words {
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seconds: 30
nanos: 500000000
}
end_time {
seconds: 30
nanos: 700000000
}
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}
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nanos: 700000000
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}
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}
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seconds: 33
nanos: 100000000
}
word: "\340\264\265\340\264\264\340\264\277"
}
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seconds: 33
nanos: 100000000
}
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seconds: 33
nanos: 200000000
}
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}
words {
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nanos: 200000000
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nanos: 300000000
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}
words {
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seconds: 33
nanos: 300000000
}
end_time {
seconds: 33
nanos: 700000000
}
word: "\340\264\244\340\264\260\340\264\276\340\264\256\340\265\213"
}
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seconds: 33
nanos: 700000000
}
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seconds: 34
nanos: 800000000
}
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nanos: 800000000
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nanos: 300000000
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nanos: 200000000
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nanos: 600000000
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}
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seconds: 38
nanos: 600000000
}
end_time {
seconds: 38
nanos: 900000000
}
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}
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nanos: 900000000
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seconds: 39
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}
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nanos: 400000000
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words {
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seconds: 39
nanos: 400000000
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nanos: 700000000
}
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nanos: 700000000
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nanos: 300000000
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}
words {
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seconds: 41
nanos: 300000000
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end_time {
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nanos: 800000000
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}
words {
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nanos: 800000000
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end_time {
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nanos: 100000000
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word: "\340\264\257\340\264\234\340\265\215\340\264\236\340\264\202"
}
}
result_end_time {
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nanos: 670000000
}
language_code: "ml-in"
}
results {
alternatives {
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end_time {
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nanos: 300000000
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speaker_tag: 1
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nanos: 300000000
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total_billed_time {
seconds: 61
}

[sukumarpm]

I tried to enable Speaker Diarization. I am able to see only "speaker_tag:1". When i refer with the actual audio file, words from other speakers are missing. As per the documentation here , i have changed only the file name, AudioEncoding.MP3 and language_code="ml-IN" only. Please see below the response. Kindly let me know if you need my audio which i am testing.

response: results {
alternatives {
transcript: "\340\264\241\340\265\215\340\264\260\340\265\210\340\264\265\340\265\274 \340\264\265\340\264\243\340\265\215\340\264\237\340\264\277 \340\264\250\340\264\277\340\265\274\340\264\244\340\265\215\340\264\244\340\264\277"
confidence: 0.8085001
words {
start_time {
seconds: 2
}
end_time {
seconds: 4
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[sukumarpm]

I tried to enable Speaker Diarization. I am able to see only "speaker_tag:1". When i refer with the actual audio file, words from other speakers are missing. As per the documentation here , i have changed only the file name, AudioEncoding.MP3 and language_code="ml-IN" only. Kindly let me know if you need my audio which i am testing.

[sukumarpm]

Hi There, any suggestion on my query?

[GAInTheHouse]

@sukumarpm I had the same issue. @kvandres 's suggestion in helped me. It might also help to see my config:

diarization_config = speech.SpeakerDiarizationConfig(
    enable_speaker_diarization=True,
    min_speaker_count=2,
    max_speaker_count=2,
    )
    metadata = speech.RecognitionMetadata(
        interaction_type = speech.RecognitionMetadata.InteractionType(1)
    )
 
config = speech.RecognitionConfig(
      encoding=speech.RecognitionConfig.AudioEncoding.WEBM_OPUS,
      enable_automatic_punctuation=True,
      language_code="en-US",
      diarization_config=diarization_config,
      metadata = metadata,
      model = "video"
    )

 # Send async recognition request
 operation = client.long_running_recognize(config=config, audio=audio)