WAV FILE stringclasses 10
values | EXPECTED OUTPUT stringclasses 10
values | MODEL'S OUTPUT stringclasses 10
values |
|---|---|---|
common_voice_sw_27777482.wav | Kwa nchi ya Tanzania, tazama tabia ya nchi Tanzania. | Kwa nchi ya Tanzania, tazama tabia ya nchi Tanzania. |
common_voice_sw_27777484.wav | Alifanya biashara ya kahawa,dhahabu, meno ya tembo na silaha | Halifanya biyashara ya kahawa, rahabu, meno ya tembo na silaha. |
common_voice_sw_27777513.wav | Aliwahi kucheza timu ya taifa ya marekani | Aliwahi kucheza timu ya taifa ya Amerikani. |
common_voice_sw_27777514.wav | Mwenyewe aliuawa kwa tendo hilo | Mwenyewe aliwawa kwa tendo hilo. |
common_voice_sw_27777609.wav | Jina la ziwa linatokana na mji mkubwa ulipo kando yake | Jinalazi wa linatokana na mji mkubwa ulipo kando yake. |
common_voice_sw_27777611.wav | Sehemu fulani ya tofauti kati ya jinsia mbili hutegemea pia utamaduni | Sehemu flani ya tofauti kati ya jinsi ya mbili hutegemea pia utamaduni. |
common_voice_sw_27777612.wav | Alipata shahada ya juu ya kiingereza kutoka shuleni
| Alipata shahada ya juu ya kiingereza kutuka shuleni. |
common_voice_sw_27777624.wav | Na hii ndio ilikuwa sababu ya yeye kuwa katika hatari ya Maisha yake | Na hii ndiyo ilikuwa sababu ya yeye kuwa katika hatari ya maisha yake. |
common_voice_sw_27777625.wav | Idadi ya watu ni waislamu lakini pia ina wakatoliki na wahuishaji | Idadi ya watu ni wa Islamu lakini pia ina wakatoliki na wahuhishaaji. |
common_voice_sw_27777455.wav | Sehema kubwa ya meru ina Ardhi ya rotuba na mvua ya kutosha | Sehemu kubwa ya meru ina athi ya rutuba na mvuwa ya kutosha. |
model: https://huggingface.co/zenlm/zen3-asr
Code:
from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor import torch import librosa import numpy as np
model_id = "zenlm/zen3-asr" processor = AutoProcessor.from_pretrained(model_id) model = AutoModelForSpeechSeq2Seq.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto")
def transcribe(audio_path): audio, sr = librosa.load(audio_path, sr=16000)
<!-- Pass raw waveform directly to processor -->
inputs = processor(
audio,
return_tensors="pt",
sampling_rate=16000,
)
<!-- Cast to model's dtype and move to device -->
input_features = inputs.input_features.to(model.device).to(model.dtype)
<!-- Generate output from the model -->
outputs = model.generate(input_features)
<!-- Decode the output and return transcription -->
return processor.batch_decode(
outputs,
skip_special_tokens=True
)[0]
test_files = [ "/content/common_voice_sw_27777455.wav", "/content/common_voice_sw_27777482.wav", "/content/common_voice_sw_27777484.wav", "/content/common_voice_sw_27777513.wav", "/content/common_voice_sw_27777514.wav", "/content/common_voice_sw_27777609.wav", "/content/common_voice_sw_27777611.wav", "/content/common_voice_sw_27777612.wav", "/content/common_voice_sw_27777624.wav", "/content/common_voice_sw_27777625.wav" ]
for f in test_files: print(f"File: {f}") print(f"Transcription: {transcribe(f)}") print("---")
Blind Spots Explanation:
Example Blind Spot: For instance, you observed that the model frequently transcribes "mvua" (rain) as "mvuwa". This could be due to phonetic similarities in the words or the model’s training data containing noisy or ambiguous pronunciation data.
Possible Causes: The model may be failing to correctly differentiate between words with similar sounds or may have difficulty in recognizing regional variations in pronunciation.
Fine-Tuning Dataset: Discuss the kind of dataset that could help fine-tune the model. For instance:
Additional Audio Samples: To fix this, the model could benefit from more diverse speech data with correct pronunciation of words like "mvua" and "mvuwa" in various accents or contexts.
Data Size: A dataset of around 10,000 to 50,000 diverse Swahili sentences containing regional variations, slang, and different accents would likely be needed.
Collecting Data: You could source data from local Swahili speakers, using resources like Common Voice or recording local voices from diverse regions.
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